Detecting an analyte in a medium

ABSTRACT

The invention generally relates to detecting an analyte in a medium. In certain aspects, the invention provides systems and methods for detecting an analyte in a medium comprising one or more light-emitting diodes, each operating at a single wavelength in a deep ultraviolet (UV) range for excitation of a target in a medium and a plurality of semiconductor photodetectors. The system is configured such that each semiconductor photodetector detects only a subset of emission from the excited analyte in the medium. In some examples, systems and methods of the invention comprise a light-emitting diode and a semiconductor photodetector for detection of the absence or presence of a non-specific contaminant.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of and priority to U.S. ProvisionalPatent Application No. 62/679,609 filed Jun. 1, 2018, the contents ofwhich are incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The invention generally relates to apparatuses and methods for detectingan analyte in a medium.

BACKGROUND

The presence of target analytes in a sample, along with theidentification and levels of target analytes that are present in thesample can provide valuable information in a number of industries. Forexample, analysis of a water sample for various components, such aspathogens or chemicals, and their respective concentrations can beindicative of the presence of potentially dangerous levels of a certaincomponent or contamination. The contamination information may be helpfulwhen determining treatment options for the water source.

Currently available testing methods for contaminants in water sources,on food, or on surfaces are complex, expensive, and slow. The testingmethods and related equipment usually have a high capital cost and arenot portable, but instead must be set up within a laboratory. Inaddition to being bulky and expensive, the testing processes may includeseveral steps that are not able to be carried out by a general member ofthe public, such as filtering, culturing, incubation, and staining.

Further, the testing process itself can take days or weeks to produceresults. In that time, a minor contamination problem may turn into amajor event. Public water resources may be infected, food productionlines may be contaminated, and hospital infections may spread quickly.

SUMMARY

The invention recognizes that there is a need for quick, affordable,easy to use testing systems and methods for detecting a target analyte(e.g., contaminating pathogen), in a medium (e.g., water). The presentinvention leverages advances in optical technology, along withproprietary optical configurations and proprietary algorithms anddatabases to provide systems and methods for testing the quality ofvarious media and identifying and quantifying contaminating agents /analytes with the media. In certain embodiments, the systems and methodsof the invention are small, portable, point-and-shoot detection systemsthat can provide results in seconds without destroying or damaging themedia to be tested (i.e., non-destructive optical scanning). Theinvention delivers real-time biological safety monitoring of processwaters and surfaces for the water, pharmaceutical, semiconductor andfood and beverage industries.

Particularly, the invention takes advantage of the fact that certainanalytes in a medium auto-fluoresce when excited with ultraviolet light(e.g., deep ultraviolet light (deep UV)). Using the proprietaryalgorithms and databases of the invention, a unique deep UV signature ofan analyte in a medium can be identified and quantified. In that manner,the invention allows users to cost effectively, quickly and easilyensure that media and certain surfaces are safe and withoutcontamination. With systems and methods of the invention, needlesscontamination is now preventable.

For example, the present invention, in certain embodiments, providessmall, portable, point-and-shoot detection systems that can detect andprovide water quality to a user in seconds. As such, a user will nothave to wait anywhere from 24 hours to two weeks or longer to obtaincontamination testing results. In this example, the present inventiondetects a range of targets, or analytes, within medium water source orsample. Examples of target analytes include pathogens, amino acids,hormones, industrial chemicals, biomarkers, and pharmaceuticals. Assuch, this exemplary embodiment provides a solution to the bulky,time-consuming testing methods offered for water quality andcontamination testing. The system may also provide an indication andconcentration of analytes, such as pathogens or contaminants, in thewater. A system according to the present invention may allow users tohave results in seconds instead of hours, days, or weeks. This willallow users to ensure the water, they use is safe and withoutcontamination.

In an aspect, the present invention provides a system for detecting atarget in a medium. The system comprises a light-emitting diode (e.g.,one or more light emitting diodes) operating at a single wavelength in adeep ultraviolet (UV) range for excitation of a target in a medium and aplurality of semiconductor photodetectors. Optionally, one or morewavelengths for excitation may be outside of the deep UV region, forexample at 340 nm. The system is configured such that each semiconductorphotodetector detects only a subset of emission from the excited target.While excitation may be in the deep UV region, emission may be in the UVregion, such as in the UVA and UVB regions. In a preferred embodiment,the emission is in a detection range of 300-400 nm.

In certain embodiments, the system configuration for each semiconductorphotodetector detecting only a subset of emission from the excitedtarget comprises each semiconductor photodetector having a differentfilter applied thereto or a grating element to split the emission fromthe excited target such that each semiconductor photodetector detectingonly a subset of emission from the excited target. In a preferredembodiment, the system comprises at least six semiconductorphotodetectors. In an exemplary embodiment, the plurality ofsemiconductor photodetectors are avalanche photodiode detectors orsilicon sensors.

In an embodiment, the system further comprises a processor configured toprocess data received from the plurality of semiconductorphotodetectors. The processor may be integrated into the system. Theprocessor may be remote from the system. The processor may be acomputer, smart phone, or microcontroller.

In certain embodiments, the system of the present invention is aportable, handheld, point-and-shoot system.

In another aspect, the present invention is directed to methods ofproviding information regarding a medium. The methods may involveproviding a system comprising a light-emitting diode operating at asingle wavelength in a deep ultraviolet (UV) range for excitation of atarget in a medium and a plurality of semiconductor photodetectors.Optionally, one or more wavelengths for excitation may be outside of thedeep UV region, for example at 340 nm. The system may be configured suchthat each semiconductor photodetector detects only a subset of emissionfrom the excited target. A medium comprising one or more target analytesmay be exposed to at least a single wavelength in the deep UV spectrumfrom the light-emitting diode of the system to thereby excite the targetanalyte in the medium. The method may further comprise detectingemission from the excited one or more target analytes via the pluralityof semiconductor photodetectors of the system to thereby produceemission data and processing the emission data, thereby providinginformation regarding the medium.

In certain embodiments, the medium may be selected from the groupconsisting of a biofluid, water, an aluminum surface, a stainless steelsurface, a granite surface, a ceramic surface, a plastic surface, and ametallic surface. In an embodiment, the target analyte may be selectedfrom the group consisting of a microorganism, a biomolecule, and achemical. In a preferred embodiment, the medium is water and the targetanalyte is one or more pathogens.

In an embodiment, the method is performed in Earth's atmosphericconditions. In certain embodiments, the method is performed outside ofEarth's atmospheric conditions. In an embodiment, processing theemission data may comprise identifying presence of one or more targetanalytes in the medium. Processing the emission data may furthercomprise identifying the one or more target analytes in the medium.Processing the emission data may further comprise quantifying the one ormore target analytes in the medium.

In certain embodiments, the invention is directed to a system fordetecting a target in a water source. The system comprises alight-emitting diode operating at a single wavelength in a deepultraviolet (UV) range for excitation of a target in a water source; anda semiconductor photodetector that detects emission from the excitedtarget and provides a readout if a detection level exceeds a threshold.The system is provided in a housing sized and configured to mate with atop of a drinking glass. In some embodiments, the housing has a unitaryconfiguration with a conical shape. In some embodiments, the housingcomprises a plurality of components including a base or tripod. Thesystem is a portable, handheld, point-and-shoot system. The thresholddetection level is a total microbial load or a bioburden. The emissionis in a detection range of 300-400 nm.

The semiconductor photodetector is an avalanche photodiode detector or asilicon sensor. The system further comprises a processor configured toprocess data received from the semiconductor photodetector. Theprocessor is integrated into the system. The processor is remote fromthe system. The processor is a computer, smart phone, ormicrocontroller.

In certain embodiments, the invention is directed to a system fordetecting a target in a water source. The system comprises alight-emitting diode operating at a single wavelength in a deepultraviolet (UV) range for excitation of a target in a water source; anda semiconductor photodetector that detects emission from the excitedtarget and provides a readout if a detection level exceeds a threshold.The system is configured to be coupled in-line to the water source.

The threshold detection level is a total microbial load or a bioburden.The emission is in a detection range of 300-400 nm. The semiconductorphotodetector is an avalanche photodiode detector or a silicon sensor.

The system further comprises a processor configured to process datareceived from the semiconductor photodetector. The processor isintegrated into the system. The processor is remote from the system. Theprocessor is a computer, smart phone, or microcontroller.

In certain embodiments, the invention is directed to a method ofproviding information regarding a medium. The method comprises providinga system comprising a light-emitting diode operating at a singlewavelength in a deep ultraviolet (UV) range for excitation of a targetin a medium, and a semiconductor photodetector that detects emissionfrom the excited target, the system configured to be coupled in-line tothe medium.

The method further comprises exposing a medium comprising one or moretarget analytes to at least a single wavelength in the deep UV spectrumfrom the light-emitting diode of the system to thereby excite the targetanalyte in the medium; detecting emission from the excited one or moretarget analytes via the semiconductor photodetector to thereby produceemission data; and outputting a read if the emission data exceeds athreshold detection level, thereby providing information regarding themedium. The method further comprises displaying on a graphical userinterface results of the processing step.

Processing the emission data comprises identifying presence of one ormore target analytes in the medium. Processing the emission data furthercomprises identifying the one or more target analytes in the medium.Processing the emission data further comprises quantifying the one ormore target analytes in the medium.

The medium is selected from the group consisting of a biofluid, water,an aluminum surface, a stainless steel surface, a granite surface, aceramic surface, a plastic surface, and a metallic surface. The methodmay be performed in Earth's atmospheric conditions. The method may beperformed outside of Earth's atmospheric conditions.

In some embodiments, the threshold detection level is a total microbialload or a bioburden. The target analyte is selected from the groupconsisting of a microorganism, a biomolecule, and a chemical. In someembodiments, the medium is water and the target analyte is one or morepathogens.

In certain embodiments, the invention is directed to a method ofproviding information regarding a medium. The method comprises providinga system comprising a light-emitting diode operating at a singlewavelength in a deep ultraviolet (UV) range for excitation of a targetin a medium, and a semiconductor photodetector that detects emissionfrom the excited target, the system provided in a housing sized andconfigured to mate with a top of a drinking glass. In some embodiments,the housing has a unitary configuration with a conical shape. In certainembodiments, the housing comprises a plurality of components including abase or tripod. The method comprises exposing a medium comprising one ormore target analytes to at least a single wavelength in the deep UVspectrum from the light-emitting diode of the system to thereby excitethe target analyte in the medium. The method comprises detectingemission from the excited one or more target analytes via thesemiconductor photodetector to thereby produce emission data. The methodfurther comprises outputting a read if the emission data exceeds athreshold detection level, thereby providing information regarding themedium. In some embodiments, the method further comprises displaying ona graphical user interface results of the processing step.

In some embodiments, the threshold detection level is a total microbialload or a bioburden. The medium may be selected from the groupconsisting of a biofluid, water, an aluminum surface, a stainless steelsurface, a granite surface, a ceramic surface, a plastic surface, and ametallic surface. The target analyte may be selected from the groupconsisting of a microorganism, a biomolecule, and a chemical. In someexamples, the medium is water and the target analyte is one or morepathogens. The method may be performed in Earth's atmosphericconditions. The method may be performed outside of Earth's atmosphericconditions.

In an embodiment, processing the emission data comprises identifyingpresence of one or more target analytes in the medium. Processing theemission data further comprises identifying the one or more targetanalytes in the medium. Processing the emission data further comprisesquantifying the one or more target analytes in the medium.

Moreover, certain embodiments of the invention use emission data todetermine total microbial load and bioburden measurements. The presentinvention comprises directing one or more wavelengths of light that areeach within a deep ultraviolet (UV) spectrum into a medium comprising abiological substance to thereby excite the biological substance in themedium. Emission is detected from the excited biological substance viaone or more semiconductor photodetectors, thereby producing deep UVemission data. The deep UV emission data is analyzed for presence of adeep UV spectral signature indicative of the biological substance,wherein presence of the deep UV spectral signature indicates that themedium comprises a biological substance. While excitation may be in thedeep UV region, emission may be in the UV region, such as in the UVA andUVB regions.

The emission data may be used to determine total microbial load.Microbial load is the number and type of microorganisms contaminating anobject or organism, such as non-specific biological and microbiologicalcontamination. Total microbial load indicates the microbiology presentin the sample. Emission data may be analyzed for deep UV spectralsignatures indicative of microbiology. Emission data may be analyzed fordeep UV spectral signatures indicative of presence and quantity ofmicrobiology. For example, analyzing may include comparing the UVspectral signature with a library of UV spectral signatures of varyingamounts and types of microbiology on or in a variety of media. Systemsof the invention may indicate the total microbial load in the sampleafter detecting the UV spectral signatures indicative of microbiology.

In certain embodiments, the invention is used to detect total microbialload (TML). The invention is a real-time monitoring indicator of watersafety complimenting the randomized spot-check of E. coli or Coliformtest. For example, WHO and EPA waterborne disease initial screeningmethods do not detect non-coliform or protozoan pathogens such asSalmonella, Cryptosporidium, Giardia, and Listeria, among others. Theinvention can be used to detect all microbiology present in a givensample in order to provide insights that are typically undetected, evenwhen the microbiology cannot be specified. Thus, the invention adds acomplimentary layer of intelligence to current methods, such asindicating when to actually conduct a coliform test.

The emission data may be used to determine bioburden, or the number ofbacteria living on a surface or within a liquid. Often, bioburden refersto the number of microorganisms on an unsterilized surface. Emissiondata may be analyzed for deep UV spectral signatures indicative ofpresence and quantity of microorganisms. For example, analyzing mayinclude comparing the UV spectral signature with a library of UVspectral signatures of varying amounts and types of microorganisms on orin a variety of media. Systems of the invention may indicate thebioburden in the sample after detecting the UV spectral signaturesindicative of the presence or quantity of microorganisms.

In certain embodiments, the method may further comprise displaying on agraphical user interface results of the processing step.

In an aspect, the present invention is directed to a system foranalyzing a sample medium. The system comprises a processor coupled to anon-transitory memory configured to cause the system to receive sampledata associated with a sample medium, wherein the sample data comprisesidentification of a source of the sample medium and spectral data of thesample medium comprising one or more analytes. The sample data iscompared to a reference dataset comprising a plurality of referencespectra, wherein each of the plurality of reference spectra comprises aspectral profile associated with an identified medium that comprises anidentified level of one or more identified analytes in the identifiedmedium. The system according to the present invention determines whetherthe sample data matches one of the plurality of reference spectra.

In certain embodiments, if the processor determines that the sample datamatches one of the plurality of reference spectra, the processor may befurther configured to generate a sample medium quality score for thesample medium based on the identification of the one or more analytes inthe sample medium and a level of the one or more analytes in the samplemedium. The processor may be further configured to output the samplemedium quality score to a user interface.

In an embodiment, if the processor determines that the sample data doesnot match any of the plurality of reference spectra in the referencedataset, the processor may be further configured to compare the sampledata to the reference spectra in the reference dataset for an identifiedcontaminant in one or more of the reference spectra; and determinewhether the sample data matches an identified contaminant in one or moreof the plurality of reference spectra, wherein one or more matchesidentifies one or more contaminants in the sample medium.

In certain embodiments, the processor may be further configured toquantify an amount of at least one of the one or more contaminants inthe sample medium. The processor may be further configured to output anidentification and quantification of the one or more contaminants in thesample medium to a user interface. The processor may be furtherconfigured to output the sample medium quality score to a userinterface. The user interface may be integrated into the systemcomprising the processor. The user interface may be remote from thesystem comprising the processor.

In certain aspects, the present invention is directed to a system foranalyzing a sample. The system may include an excitation source forexciting a sample medium comprising one or more analytes. The system mayalso include a detector for receiving spectral data of the sample mediumcomprising the one or more analytes, and a processor operably associatedwith the sample. The processor may be coupled to a non-transitory memoryconfigured to cause the system to receive sample data associated withthe sample medium. The sample data may comprise identification of asource of the sample medium and the spectral data of the sample mediumcomprising the one or more analytes. The sample data may be compared toa reference dataset comprising a plurality of reference spectra, whereineach of the plurality of reference spectra comprises a spectral profileassociated with an identified medium that comprises an identified levelof one or more identified analytes in the identified medium. The systemof the present invention may further determine whether the sample datamatches one of the plurality of reference spectra.

In embodiments of the present invention, the processor and the userinterface may be integrated into the system. The processor and/or theuser interface may be remote from the system and/or each other. The userinterface may be integrated into the system comprising the processor.The user interface may be remote from the system comprising theprocessor. The processor may be any suitable means, such as, e.g., acomputer, smart phone, or microcontroller.

In embodiments of the present embodiment, the spectral data of thesample medium including one or more analytes may be deep ultraviolet(UV) spectral data and each of the first plurality of first referencespectra may be deep ultraviolet (UV) reference spectra.

In embodiments of the present embodiment, the system according to thepresent invention may be a portable, handheld, point-and-shoot system,which allows for ease of use for consumers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the scanner or detector according to the present invention.

FIG. 2 shows side and top views of the OLED display scanner or detectoraccording to the present application.

FIG. 3 shows the dimensions of the scanner or detector.

FIG. 4 shows the display for results of the sample.

FIG. 5 shows the user interface on an external source such as asmartphone.

FIG. 6 shows a minilab embodiment of the present invention.

FIG. 7 shows a minilab embodiment of the present invention

FIG. 8 shows wedge embodiments of the present invention.

FIG. 9 shows an embodiment using a small sample cup.

FIG. 10 shows an embodiment using a large sample.

FIG. 11 shows an embodiment using the detector as a toilet adapter.

FIG. 12 shows an embodiment using the detector using as a sink adapter.

FIG. 13 shows an embodiment of an on-line detector.

FIG. 14 shows an embodiment of an in-line detector probe.

FIG. 15 shows an embodiment of an off-line stand-off detector.

FIG. 16 shows an embodiment of a target list, or database, according tothe invention.

FIG. 17 shows limits of detection with noise and without noise.

FIG. 18 shows the signatures for filtering out microbiology.

FIG. 19 shows the water quality scaling.

FIG. 20 shows an embodiment of hardware specifications according to thebreadboard setup.

FIG. 21 shows the reference calibration target of biphenyl.

FIG. 22 shows the system block diagram of the present invention.

FIG. 23 shows the timing concept of the present invention.

FIG. 24 shows an embodiment of an algorithm used in the presentinvention.

FIG. 25 shows EEM for tap water and pure water.

FIG. 26 shows bacteria spectral signatures in tap water.

FIG. 27 shows fruit and vegetable pesticide scans.

FIG. 28 shows the ecosystem and communication between users, the cloudand blockchain, and the detector and processor.

FIG. 29 shows how data is secured on an embodiment using blockchain.

FIG. 30 shows monitoring devices upstream and downstream of a polluter.

FIG. 31 shows the Mahalanobis Distance plots of emission-excitationmatrix (EEM) spectra for bacterial and amino acid signatures.

FIG. 32 shows clustering of spectra of gram+ and gram− bacteria species.

FIG. 33 shows fluorescence of clean, distilled water.

FIG. 34 shows fluorescence of contaminated restaurant water.

FIG. 35 shows fluorescence signatures of amino acids and microbiology.

FIG. 36 compares current technology to advancements of the invention(Orb).

FIG. 37 shows a concentration curve.

FIG. 38 shows deconvolution of a bacterial mixture.

FIG. 39 shows a table of R² predicted vs. actual composition.

FIG. 40 shows the spectral profile for E. coli when viable (live) andconfirmed non-viable (dead) after autoclaving.

FIG. 41 shows the emission center wavelength for various bacterialspecies when viable (live) and confirmed non-viable (dead) afterautoclaving.

FIG. 42 shows a table of R² predicted vs actual viability.

FIG. 43 shows different sources for detection using the invention (Orb)and the EPA approved method (coliform/E. coli ).

FIG. 44 shows an outline of a test method of the invention where asource was doped with salmonella, the invention was used to detectcontamination, and the approved Gold Standard EPA method was used todetect contamination.

FIG. 45 shows results of the comparison of detection using the invention(Orb) to the Gold Standard detection.

FIG. 46 shows a selection of detection capabilities to date.

DETAILED DESCRIPTION

Various compounds with certain chemical structures can give strongauto-fluorescence or “native” fluorescence when excited with ultravioletlight. This can be quite strong for some interesting compounds such asplasticizers that have been identified as endocrine disrupters as wellas amino acids that are found in bacterial cells. By using thisphenomenon, a detection apparatus can be assembled with relativelyinexpensive and robust components that use a technique that allow thefinal device to be non-invasive, portable, and easy to use for theconsumer. Taken together, the ideal application for this technique is inthe detection, identification, and quantification of one or moreanalytes in a medium, e.g., pathogen and other contaminating agents /analytes in water, bio-fluids, and surfaces, particularly where thecurrent EPA/FDA approved process involves laboratory testing.

The present invention allows for detection results in seconds. Incertain embodiments, devices of the present invention are portable andachieve non-contact analysis. No preparation or reagents are required,and the present invention may detect multiple contaminants. The presentinvention allows detection of targets in media such as water, and alsoallows for detection of targets on surfaces such as aluminum andstainless steel surfaces. The invention delivers real-time biologicalsafety monitoring of process waters and surfaces for the water,pharmaceutical, semiconductor and food and beverage industries.

Hardware

With the advent of cheaper and more powerful ultraviolet light emittingdiodes (UV LEDS) and sensitive detectors, the present invention may beused to identify specific molecules with a high degree of accuracy in aportable, reagent-less, non-invasive manner.

In an aspect, the present invention provides a system for detecting atarget in a medium. The system comprises a light-emitting diodeoperating at a single wavelength in a deep ultraviolet (UV) range forexcitation of a target in a medium and a plurality of semiconductorphotodetectors. The system is configured such that each semiconductorphotodetector detects only a subset of emission from the excited target.In a preferred embodiment, the emission is in a detection range of300-400 nm. Deep UV is ultraviolet light below 280 nm, or ultravioletlight in the 240-280 nm range. Autofluorescence is “native” fluorescenceor emission of light by biological structures when the biologicalstructures have absorbed light or have been excited with ultravioletlight. In the present invention, the pathogens or contaminantsautofluorescence after being excited by, or absorbing, deep ultravioletlight. The emission of the autofluorescence is then detected by theplurality of detectors in the range of 300-400 nm.

In certain embodiments, the system configuration for each semiconductorphotodetector detecting only a subset of emission from the excitedtarget comprises each semiconductor photodetector having a differentfilter applied thereto or a grating element to split the emission fromthe excited target such that each semiconductor photodetector detectingonly a subset of emission from the excited target. In a preferredembodiment, the system comprises at least six semiconductorphotodetectors. In an embodiment, the plurality of semiconductorphotodetectors are avalanche photodiode detectors or silicon sensors.

In an embodiment, the system further comprises a processor configured toprocess data received from the plurality of semiconductorphotodetectors. The processor may be integrated into the system. Theprocessor may be remote from the system. The processor may be acomputer, smart phone, or microcontroller.

In certain embodiments, the system of the present invention is aportable, handheld, point-and-shoot system.

In certain embodiments, the invention is directed to a system fordetecting a target in a water source. The system comprises alight-emitting diode operating at a single wavelength in a deepultraviolet (UV) range for excitation of a target in a water source; anda semiconductor photodetector that detects emission from the excitedtarget and provides a readout if a detection level exceeds a threshold.The system is provided in a housing sized and configured to mate with atop of a drinking glass. In some embodiments, the housing has a unitaryconfiguration with a conical shape. In some embodiments, the housingcomprises a plurality of components including a base or tripod. Thesystem is a portable, handheld, point-and-shoot system. The thresholddetection level is a total microbial load or a bioburden. The emissionis in a detection range of 300-400 nm.

The semiconductor photodetector is an avalanche photodiode detector or asilicon sensor. The system further comprises a processor configured toprocess data received from the semiconductor photodetector. Theprocessor is integrated into the system. The processor is remote fromthe system. The processor is a computer, smart phone, ormicrocontroller.

In certain embodiments, the invention is directed to a system fordetecting a target in a water source. The system comprises alight-emitting diode operating at a single wavelength in a deepultraviolet (UV) range for excitation of a target in a water source; anda semiconductor photodetector that detects emission from the excitedtarget and provides a readout if a detection level exceeds a threshold.The system is configured to be coupled in-line to the water source.

The threshold detection level is a total microbial load or a bioburden.The emission is in a detection range of 300-400 nm. The semiconductorphotodetector is an avalanche photodiode detector or a silicon sensor.

The system further comprises a processor configured to process datareceived from the semiconductor photodetector. The processor isintegrated into the system. The processor is remote from the system. Theprocessor is a computer, smart phone, or microcontroller.

In certain embodiments, the invention is directed to a method ofproviding information regarding a medium. The method comprises providinga system comprising a light-emitting diode operating at a singlewavelength in a deep ultraviolet (UV) range for excitation of a targetin a medium, and a semiconductor photodetector that detects emissionfrom the excited target, the system configured to be coupled in-line tothe medium.

The method further comprises exposing a medium comprising one or moretarget analytes to at least a single wavelength in the deep UV spectrumfrom the light-emitting diode of the system to thereby excite the targetanalyte in the medium; detecting emission from the excited one or moretarget analytes via the semiconductor photodetector to thereby produceemission data; and outputting a read if the emission data exceeds athreshold detection level, thereby providing information regarding themedium. The method further comprises displaying on a graphical userinterface results of the processing step.

Processing the emission data comprises identifying presence of one ormore target analytes in the medium. Processing the emission data furthercomprises identifying the one or more target analytes in the medium.Processing the emission data further comprises quantifying the one ormore target analytes in the medium.

The medium is selected from the group consisting of a biofluid, water,an aluminum surface, a stainless steel surface, a granite surface, aceramic surface, a plastic surface, and a metallic surface. The methodmay be performed in Earth's atmospheric conditions. The method may beperformed outside of Earth's atmospheric conditions.

In some embodiments, the threshold detection level is a total microbialload or a bioburden. The target analyte is selected from the groupconsisting of a microorganism, a biomolecule, and a chemical. In someembodiments, the medium is water and the target analyte is one or morepathogens.

In certain embodiments, the invention is directed to a method ofproviding information regarding a medium. The method comprises providinga system comprising a light-emitting diode operating at a singlewavelength in a deep ultraviolet (UV) range for excitation of a targetin a medium, and a semiconductor photodetector that detects emissionfrom the excited target, the system provided in a housing sized andconfigured to mate with a top of a drinking glass. In some embodiments,the housing has a unitary configuration with a conical shape. In certainembodiments, the housing comprises a plurality of components including abase or tripod. The method comprises exposing a medium comprising one ormore target analytes to at least a single wavelength in the deep UVspectrum from the light-emitting diode of the system to thereby excitethe target analyte in the medium. The method comprises detectingemission from the excited one or more target analytes via thesemiconductor photodetector to thereby produce emission data. The methodfurther comprises outputting a read if the emission data exceeds athreshold detection level, thereby providing information regarding themedium. In some embodiments, the method further comprises displaying ona graphical user interface results of the processing step.

In some embodiments, the threshold detection level is a total microbialload or a bioburden. The medium may be selected from the groupconsisting of a biofluid, water, an aluminum surface, a stainless steelsurface, a granite surface, a ceramic surface, a plastic surface, and ametallic surface. The target analyte may be selected from the groupconsisting of a microorganism, a biomolecule, and a chemical. In someexamples, the medium is water and the target analyte is one or morepathogens. The method may be performed in Earth's atmosphericconditions. The method may be performed outside of Earth's atmosphericconditions.

In an embodiment, processing the emission data comprises identifyingpresence of one or more target analytes in the medium. Processing theemission data further comprises identifying the one or more targetanalytes in the medium. Processing the emission data further comprisesquantifying the one or more target analytes in the medium.

In certain embodiments, the invention is directed to a system fordetermining that a medium comprises a biological substance. The systemcomprises a housing with a built-in display, the housing sized andconfigured to mate with a top of a drinking glass. In certainembodiments, the housing has a unitary configuration with a conicalshape. In some embodiments, the housing has a plurality of componentsincluding a base or tripod.

The system comprises one or more excitation sources disposed in thehousing, each operating in a deep ultraviolet (UV) range for excitationof a biological substance in a medium. The system further comprises oneor more detectors comprising a semiconductor photodetector, the one ormore detectors disposed in the housing. The system is configured suchthat the semiconductor photodetector detects emission from the excitedbiological substances and displays a reading on the built-in display,wherein the reading is dependent on whether the emission exceeds athreshold detection level. The emission is in a detection range of300-400 nm. The system is a portable, handheld, point-and-shoot system.

The system further comprises a processor configured to process datareceived from the semiconductor photodetector. In certain embodiments,the processor is integrated into the system. In some embodiments, theprocessor is remote from the system. The processor may be a computer,smart phone, or microcontroller.

The threshold detection level may be a bioburden or total microbialload. The biological substance may be a pathogen and the system may beconfigured such that the semiconductor photodetector detects only asubset of emission from the excited pathogen to produce a deep UVspectral signature indicative of presence of the pathogen in the medium.

In an embodiment, the invention is directed to a system for determiningthat a medium comprises a biological substance. The system comprises oneor more excitation sources, each operating in a deep ultraviolet (UV)range for excitation of a biological substance in a medium. The systemcomprises one or more detectors comprising a semiconductorphotodetector. In embodiments of the invention, the emission is in adetection range of 300-400 nm.

The system further comprises a housing, the one or more excitationsources and the one or more detectors disposed in the housing, and anadapter operable with the housing, the adapter configured to bereleasably attachable to a supply source for the medium. In certainembodiments, the housing has a unitary configuration with a conicalshape. In some embodiments, the housing has a plurality of componentsincluding a base or tripod. The system is configured such that thesemiconductor photodetector detects emission from the excited biologicalsubstances and outputs a reading, the reading dependent on whether theemission exceeds a threshold detection level. In some embodiments, theadapter is releasably attachable to a pipe. In some embodiments, theadapter is a tap mount for a faucet.

The system further comprises a processor configured to process datareceived from the semiconductor photodetector. In certain embodiments,the processor is integrated into the system. In some embodiments, theprocessor is remote from the system. The processor may be a computer,smart phone, or microcontroller.

The threshold detection level may be a bioburden or total microbialload. The biological substance may be a pathogen and the system may beconfigured such that the semiconductor photodetector detects only asubset of emission from the excited pathogen to produce a deep UVspectral signature indicative of presence of the pathogen in the medium.

In an embodiment, the invention is directed to a system for determiningthat a medium comprises a biological substance. The system comprises oneor more excitation sources, each operating in a deep ultraviolet (UV)range for excitation of a biological substance in a medium. The systemcomprises one or more detectors comprising a semiconductorphotodetector. In embodiments of the invention, the emission is in adetection range of 300-400 nm.

The system further comprises a housing, the one or more excitationsources and the one or more detectors disposed in the housing, and anadapter operable with the housing, the adapter configured to bereleasably attachable to a supply source for the medium. The system isconfigured such that the semiconductor photodetector detects emissionfrom the excited biological substances and outputs a reading, thereading dependent on whether the emission exceeds a threshold detectionlevel.

In certain embodiments, the housing has a unitary configuration with aconical shape. In some embodiments, the housing has a plurality ofcomponents including a base or tripod. In some embodiments, the adapteris releasably attachable to a pipe. In some embodiments, the adapter isa tap mount for a faucet.

The system further comprises a processor configured to process datareceived from the semiconductor photodetector. In certain embodiments,the processor is integrated into the system. In some embodiments, theprocessor is remote from the system. The processor may be a computer,smart phone, or microcontroller.

The threshold detection level may be a bioburden or total microbialload. The biological substance may be a pathogen and the system may beconfigured such that the semiconductor photodetector detects only asubset of emission from the excited pathogen to produce a deep UVspectral signature indicative of presence of the pathogen in the medium.

In an embodiment, the invention is directed to a method for determiningthat a medium comprises a biological substance. The method comprisesdirecting one or more wavelengths of light that are each within a deepultraviolet (UV) spectrum into a medium comprising a biologicalsubstance to thereby excite the biological substance in the medium. Themethod comprises detecting emission from the excited biologicalsubstance via one or more semiconductor photodetectors, each operatingin a deep ultraviolet (UV) range for excitation of the biologicalsubstance in the medium, thereby producing deep UV emission data. Themethod further comprises analyzing the deep UV emission data forpresence of a deep UV spectral signature indicative of the biologicalsubstance, wherein presence of the deep UV spectral signature indicatesthat the medium comprises a biological substance.

In an embodiment, the emission is in a detection range of 300-400 nm.The one or more semiconductor photodetectors is an avalanche photodiodedetector or a silicon sensor.

In certain aspects, the medium is selected from the group consisting ofa biofluid, water, an aluminum surface, a stainless steel surface, and ametallic surface. In some examples, the biological substance is apathogen. In some instances, the biological substance is a pathogen andthe medium is water. The method may be performed in Earth's atmosphericconditions. The method may be performed outside of Earth's atmosphericconditions.

In an embodiment, the invention is directed to a method for identifyinga pathogen in a medium. The method comprises directing one or morewavelengths of light into a medium comprising a pathogen and anon-pathogen biological substance to thereby excite the pathogen and thenon-pathogen biological substance in the medium; and detecting emissionusing one or more detectors comprising a semiconductor photodetectorthat detects different wavelengths of emission such that a spectralsignature unique to the pathogen is detected and distinguished from aspectral signature of the non-pathogen biological substance, therebyidentifying the pathogen in the medium. The method further comprisesquantifying an amount of the pathogen in the medium. The method furthercomprises generating a quality value of the medium.

In some embodiments, the non-pathogen biological substance is a protein.In some embodiments, the pathogen is a live pathogen. In certainexamples, the spectral signature unique to the pathogen is a spectralsignature unique to the live pathogen. In some examples, the spectralsignature unique to the live pathogen is detected and distinguished froma spectral signature of the pathogen when dead.

In certain embodiments, the medium is selected from the group consistingof a biofluid, water, an aluminum surface, a stainless steel surface, agranite surface, a ceramic surface, a plastic surface, and a metallicsurface. The one or more wavelengths of light are within a deepultraviolet (UV) range. The emission is detected at a range of 300-400nm.

In an embodiment, the invention is directed to a method for identifyinga plurality of pathogens in a medium. The method comprises directing oneor more wavelengths of light into a medium comprising a plurality ofpathogens and a non-pathogen biological substance to thereby excite theplurality of pathogens and the non-pathogen biological substance in themedium; and detecting emission using one or more detectors comprising asemiconductor photodetector that detects different wavelengths ofemission such that a spectral signature unique to each of the pluralityof the pathogens is detected and the spectral signature unique to eachof the plurality of the pathogens is distinguished from each other and aspectral signature of the non-pathogen biological substance, therebyidentifying each of the plurality of pathogens in the medium. The methodfurther comprises quantifying an amount of the each of the plurality ofpathogens in the medium. The method further comprises generating aquality value of the medium.

In certain embodiments, the non-pathogen biological substance is anamino acid. In certain embodiments, at least one pathogen of theplurality of pathogens is a live pathogen. The spectral signature uniqueto the pathogen may be a spectral signature unique to the live pathogen.In some instances, the spectral signature unique to the live pathogen isdetected and distinguished from a spectral signature of the pathogenwhen dead.

In certain embodiments, the medium is selected from the group consistingof a biofluid, water, an aluminum surface, a stainless steel surface, agranite surface, a ceramic surface, a plastic surface, and a metallicsurface. The one or more wavelengths of light are within a deepultraviolet (UV) range. The emission is detected at a range of 300-400nm. As shown in FIG. 22, the system block diagram depicts a sampletarget being subjected to UV LED. Detector 1 of N detectors detects thesignal from the sample target and sends the signal to the Amplifier & Ato D (analog to digital) Converter to be amplified and converted todigital. The signal then goes to the gated pulse controller and then onto the master control. The master control may be any suitable means andpreferably may be a laptop, cell phone, or microcontroller and softwarefor processing. The master control is in communication with the spectrallibrary and the calibration standards of the present invention. Themaster control may send results via Bluetooth LE, smartphones, andpersonal computers. The master control also communicates with the pulseselection for amplitude, duration and rep rate (1-10 Hz). The pulseselection communications to the LED pulse power supply which inputs tothe UV LED. The pulse selection also communicates with the detectortiming gate control for the ambient and signal timing. A filter wheelmay optionally be arranged between the sample target and Detector 1 of Ndetectors.

The timing concept of the present invention is shown in FIG. 23. Anominal full cycle is 1 to 5 Hz. the LED duty cycle is less than 3%. Atthe zero time reference point, the LED and Detectors are OFF. Once theLED and Detectors are ON, the LED rise/fall should be less than 5 μs,with 0 to full, or full to 0 as options. The ambient and signalmeasurements are the same length of time.

Multiple system configurations are further discussed and exemplifiedherein and the skilled artisan will appreciate that the configurationsare exemplary and non-limiting embodiments of the invention. In aparticular exemplary embodiment, the present invention identifies andquantifies certain targets using a single wavelength excitation and six(6) channel detection between the 300-400 nm range. This involvescharacterizing the spectral properties of these targets. In certainembodiments, the invention uses single channel detection, or 1 channeldetection. Single channel detection allows for indication of presence orabsence of a biological substance or microorganism.

In order to determine the feasibility of using native fluorescence todetect potential targets at low concentrations, a screening protocol wasconstructed to determine various criteria to identify possible targets.A photometric standard was created to correlate various detectionschemes (spectrophotometers/various detectors/various optical layout),which is not commercially available. This allows for determination ofpossible limits of detection (in parts per million (ppm)/parts perbillion) depending on hardware parameters (PMT/Si Detector). Thepotential targets may be determined (see FIG. 16 for exemplary targets).Potential challenges addressed and overcome include environmentalfactors (pH, salt, temperature), and quenching at lower concentrationsthan previously reported.

A model was created based on the concentration study of individualtargets in tap water. The robustness of the model was tested byartificially added noise and determining limits of detection. Asimulated engine was created based on real data to generate initialhardware parameters (band passes, optimized laser excitationsignal/noise, etc.) and test robustness of initial quantificationalgorithm. From samples made in lab, BPA was able to be detected andquantified down to 0.023 ppm cross-validated with optimized laserexcitation and band passes. A key finding for the algorithm developmentwas to be able to construct a library for quantification of new samples.The calibration library in house may quantify BPA down to 0.023 ppm inwater with a similar environment (FIG. 17).

The bandpass configuration was determined that would separate outmicrobiology from each other and from amino acid signatures. Certainexcitations separate out different microbiological strains from eachother as well as raw amino acid signatures using six (6) channeldetection (FIG. 18).

Water quality may be discerned with a single excitation and six (6)channel detection of native auto fluorescence. Various hardwareconfigurations provide the water quality information. Finaldetermination of water quality may be verified by a water lab (FIG. 19).

In certain embodiments, the hardware specifications included thefollowing examples for LED and detectors. As an exemplary embodiment,the LED was selected from continuous mode—100 mA˜1 mW and pulsed mode, 4Hz, 2% Duty cycle (5 ms on). The max drive current was ˜350 mA with ThorLab power supply and Rigol pulse generator.

As an exemplary embodiment, the detector was selected from HamamatsuS12698-01 photodiode, Hamamatsu MPPC, and STS-UV Ocean optics fibercoupled spectrometer (FIG. 20).

The configuration utilizes the front surface detection with an LED lightsource (278 nm) focused onto a cuvette holder and detector assemblyplaced at approximately 35 degrees away from the incoming beam tocollect the fluorescence light. With this configuration, the resultingfluorescence output of the standard biphenyl in ethanol was determinedin absolute values (uW/nanojoules). Therefore, the range of signalsexpected to be found as a function of target of interest (FIG. 21) wascalculated. The silicon detector was suitable and a preferable detectormay be the MPPC (APD). At 1 mW (100 mA) a sample, 10{circumflex over( )}2 CFU (colony forming units of bacteria) produce about 3 pW offluorescence in the 300-400 nm range. The angle dependence may furtherbe optimized.

In certain embodiments, a grating system may be used instead of filtersand how the light is split and filtered would change slightly. Using agrating option may allow access to more wavelengths of interest.

Algorithms and Software

The present invention also uses algorithms in detection, identification,and quantification of target analytes. The initial target screens forthe algorithm include determination of whether the target fluoresce inthe region of interest, e.g. in water, whether the fluorescence isstrong enough, e.g. to EPA/FDA limits, and whether samples contaminatedwith targets that are indicative of real world scenarios may beexperimentally created.

Identification, classification, and quantification are then based onfluorescence spectrum. This requires a model based on experimentallyderived data. The data in the model is correlated to and indicative ofreal world scenarios in order to ensure robustness and high confidencelevels of the models. For example, fluorescence can change based ontemperature/pH/salt other molecular interactions and models of theinvention account for various conditions (i.e. only tap water, only poolwater, etc.).

In an embodiment of the present invention, an algorithm may used by auser who identifies a source for the medium. The sample may then bescanned and compared to an in-house database of sources. If the sourceis within the “threshold”, the water quality value may be reported. Ifthe source is outside of the “threshold”, the source may be identifiedas an outlier. The source may then be compared to an in-house databaseof contaminants, which is in communication with the samples in thecontaminant and chemical libraries. If the contaminant is identified,then the contaminant may further be quantified.

FIG. 24 shows an embodiment of an algorithm used in the presentinvention. The user identifies a source for the medium. The sample isscanned. The sample is then compared to defined source, a step whichuses a library of sources that is already in-house. If the sample is notdetected as an outlier, then the water quality value is reported. If thesample is detected as an outlier, the sample is compared to the libraryof contaminants, which uses a library of contaminants in varioussources. If there is a single contaminant classification, thenquantification commences using the in-house concentration study of thecontaminant. If there was no single contaminant classification, then itis considered whether the spectra may be broken down to variouscomponents. If a known contaminant has not been identified, then a waterquality value is output. If a known contaminant has been identified,then a determination of whether the quantification is within error ismade. If the quantification is within error, then the contaminant isidentified with a concentration. If the quantification is not withinerror, then merely the contaminant is identified.

Various known statistical pattern recognition methods can be used inconjunction with the present invention. For example, the followingstatistical methods, training sets, machine learning techniques, andcomparisons to known spectra may be used.

An important feature of the methods of the invention is the ability toanalyze heterogeneous samples using a fluorescence or an absorptionspectrum. Fluorescence microscopy measures the fluorescence of aparticular compound when given a particular wavelength. As such, thewavelength that reaches the detector is a different wavelength than usedto shine the sample. Fluorescent compounds can absorb light at aparticular wavelength and emit light at a higher wavelength, with someenergy being lost by the compound to the surroundings. Absorbancespectroscopy measures how much of a particular wavelength of light getsabsorbed by a sample. It's usually used to measure the concentration ofa compound in a sample. As such, the more light that is absorbed, thehigher the concentration of the compound in the sample.

The methods for analyzing the fluorescence or absorption spectrum arebased upon the principles that each element in a mixture has its ownspectrum and that each element has a specific absorption coefficient.The methods of the invention then correlate concentration withabsorption. Particularly, the concentration of a compound can bedetermined with the knowledge of the compound's absorption coefficient.This relationship, in the most basic sense, can be illustrated by Beer'sLaw:

A=εbc,

wherein A is absorbance, c is concentration (mol/L;M), b is pathlength,and ε is the molar absorptivity (or extinction coefficient). Molarabsorptivity is the characteristic of a substance that tells how muchlight is absorbed at a particular wavelength.

When measuring the fluorescence or absorption of a heterogeneousmixture, the sum of the absorption coefficient values for each elementis measured at the same time. Thus, in order to determine theconcentration, the linear combination of all spectra of the elementsneeds to be determined. The analysis then takes into account theinteraction of elements with one another. The analysis then accounts forthe fact that despite each element having a different spectrum, theiroptical absorbance can be the same. For example, one element may bepresent at 1 mM and another may also be present at 1 mM, both of whichcan be 1000 times less than the total value, or signal, of the mixture.

In one embodiment, deconvolution can be used to enable determination ofconcentrations. Deconvolution is an algorithm-based process used toreverse the effects of convolution on recorded data. See, e.g., O'HaverT. “Intro to Signal Processing—Deconvolution”. University of Maryland atCollege Park. Retrieved 2016-09-13, the content of which is incorporatedby reference herein in its entirety. In general, the object ofdeconvolution is to find the solution of a convolution equation of theform: f*g=h, wherein h is some recorded value, and f is the desiredvalue, but has been convolved with some other value g before it wasrecorded. The function g might represent the interaction between twoelements. If g is known, then deterministic deconvolution can beperformed. However, if g is not known in advance, then it will need tobe estimated using, for example, statistical estimation. In actualpractice, the situation is usually closer to: (f*g)+ε=h, wherein ε isnoise that has entered the recorded value. The lower the signal-to-noiseratio, the worse the estimate of the deconvolved value will be.

Methods for deconvoluting the data in accordance with the presentdisclosure include the use of, for example, principal component analysis(PCA). PCA is a statistical procedure that reduces the dimensionality ofa data set by transforming the data to a new set of variable (principalcomponents) that summarize the features of the data. See, for example,Jolliffe, 1986, Principal Component Analysis, Springer, N.Y. PCA uses anorthogonal transformation to convert a set of observations of possiblycorrelated variables into a set of values of linearly uncorrelatedvariables called principal components. The number of principalcomponents is less than or equal to the number of original variables.This transformation is defined in such a way that the first principalcomponent has the largest possible variance (that is, accounts for asmuch of the variability in the data as possible), and each succeedingcomponent in turn has the highest variance possible under the constraintthat it is orthogonal to the preceding components. The resulting vectorsare an uncorrelated orthogonal basis set. PCA is sensitive to therelative scaling of the original variables. The first few principalcomponents (“PCs”) capture most of the variation in the data set. Incontrast, the last few PCs are often assumed to capture only theresidual ‘noise’ in the data. PCA is discussed in more detail below withrespect to use of databases in the analysis of data. It is also to beunderstood that other statistical analysis methods known in the art,such as those discussed in more detail below, can be used. Exemplaryanalyses are also described below.

In the present invention, the presence of a target analyte and itsconcentration may be reported. In certain embodiments, the methods ofthe invention can involve the use of a computer system (described inmore detail below) to generate a report that includes a determination ofthe presence of and concentration of the target analyte. The computersystem may perform one or more of the following steps: analyzing thesample to provide spectral data on the one or more target analytesreceived by the single detector, retrieving known spectral andconcentration data, applying the known data to the spectral datareceived by the detector, and generating a report comprising theconcentration of the one or more target analytes. The report may be sentto an output device such as a display monitor or a printer.

Converting a Fluorescence or an Absorption Spectrum to a ConcentrationReading

Sample analysis results are generally reported in concentrations ofdifferent analytes in a sample. The present disclosure provides for amethod in which spectral data can be converted into concentration for atarget analyte through the comparison of the spectral data to a databasecomprising known spectra already associated with concentration levels ofthe target analyte. Because methods of the present invention may involvethe use of a single detector that receives a light beam after it haspassed through the sample, the spectral data may include totalabsorption or fluorescence data. Optionally, more than one detector,e.g. inclusive up to at least six or more detectors, may be used.Typically, when converting spectral data to concentration, carefulmeasurement of a “training set” of samples is performed. A mathematicalmultivariate model is then constructed for individual components to beeventually used to evaluate unknown concentrations.

In certain embodiments, the database will contain chemical compositionand spectral data from a training set. The training set can comprise anumber of samples from which the chemical composition and spectralbehavior are known. Chemical composition data can be determined throughany means known in the art, such as, for example, a chemical componentanalyzer (CCA). Spectral behavior can be determined through any meansknown in the art, including the apparatuses and methods describedherein.

Using the spectral data obtained, the concentration of the components(e.g. elements of blood plasma) can be determined. This information iscompiled in a database and absorption or fluorescence/concentrationcurves for the various components/elements can be determined and alsocontained in the database.

Once the database is compiled, the concentration of one or more targetanalytes in a heterogeneous sample can be determined. This is done bycomparing the spectral data obtained according to the present disclosureto the database comprising the known spectra already associated withconcentration levels of the target analyte.

This aspect of the present disclosure is especially amenable forimplementation using a computer. The computer or CPU is able to comparethe spectral data of the target analyte(s) to the reference spectraldata to thereby provide the concentration of the target analyte(s). Suchsystems generally include a central processing unit (CPU) and storagecoupled to the CPU. The storage stores instructions that when executedby the CPU, cause the CPU to accept as input, spectral data obtained bythe detector. The executed instructions also cause the computer toprovide the concentration of the target analyte as a result of inputtingthe sample data into an algorithm, or pattern recognition platform,trained on the reference set of known spectral data.

In certain embodiments, the reference set is stored at a remote locationseparate from the computer and the computer communicates across anetwork to access the reference set in order to determine theconcentration. In other embodiments, the reference set is stored locallywithin the computer and the computer accesses the reference set withinthe computer in order to make the determination.

The pattern recognition platform can be based on any appropriate patternrecognition method that is capable of receiving input datarepresentative of a spectral data from the sample being analyzed andproviding the concentration of the target analyte in the sample as anoutput. The pattern recognition program is trained with training datafrom a reference set of known spectral data and concentrations fromvarious analytes. In some embodiments, a test sample having knownconcentration and spectral data can be used to test the accuracy of theplatform recognition platform obtained using the training data.

Various known statistical pattern recognition methods can be used inconjunction with the present disclosed methods. Suitable statisticalmethods include, without limitation, principal component analysis (PCA),logic regression, ordinal logistic regression, linear or quadraticdiscriminant analysis, clustering, nearest neighbor classifier analysis,and Cox Proportional Handling. Non-limiting examples of implementingparticular pattern recognition platforms using the various statisticalare provided herein.

In some embodiments, the pattern recognition platform is based on aregression model, preferably a logistic regression model. Someembodiments of the present invention provide generalizations of thelogistic regression model that handle multicategory (polychotomous)responses. Such embodiments can be used to discriminate between three ormore elements. Such regression models use multicategory logit modelsthat simultaneously refer to all pairs of categories, and describe theodds of response in one category instead of another. Once the modelspecifies logits for a certain (J-1) pairs of categories, the rest areredundant. See, for example, Agresti, An Introduction to CategoricalData Analysis, John Wiley & Sons, Inc., 1996, New York, Chapter 8, whichis hereby incorporated by reference.

Linear discriminant analysis (LDA) attempts to classify sample accordingto its elemental composition based on certain spectral properties. Inother words, LDA tests whether measured spectral data predictscategorization. LDA typically requires continuous independent variablesand a dichotomous categorical dependent variable. In the presentdisclosure, the spectral data for select wavelengths across a number ofelements in the training population serve as the requisite continuousindependent variables. The concentration of each of the elements of thetraining population serves as the dichotomous categorical dependentvariable.

LDA seeks the linear combination of variables that maximizes the ratioof between-group variance and within-group variance by using thegrouping information. Implicitly, the linear weights used by LDA dependon how the spectral data for a wavelength separates between, forexample, two different elements and how the spectral data correlateswith spectral data for other wavelengths. For example, LDA can beapplied to the data matrix of the N members (e.g. elements) in thetraining sample by K wavelengths in a number of wavelengths described inthe present invention. Then, the linear discriminant of each member ofthe training population is plotted. Ideally, those members of thetraining population representing a first subgroup (e.g. a first element)will cluster into one range of linear discriminant values and thosemembers of the training population representing a second subgroup (e.g.a second element) will cluster into a second range of lineardiscriminant values. The LDA is considered more successful when theseparation between the clusters of discriminant values is larger. Formore information on linear discriminant analysis, see Duda, PatternClassification, Second Edition, 2001, John Wiley & Sons, Inc; andHastie, 2001, The Elements of Statistical Learning, Springer, N.Y.;Venables & Ripley, 1997, Modern Applied Statistics with s-plus,Springer, N.Y.

Quadratic discriminant analysis (QDA) takes the same input parametersand returns the same results as LDA. QDA uses quadratic equations,rather than linear equations, to produce results. LDA and QDA areinterchangeable, and which to use is a matter of preference and/oravailability of software to support the analysis. Logistic regressiontakes the same input parameters and returns the same results as LDA andQDA.

In some embodiments of the present disclosure, decision trees are usedto classify elements using spectral data for a selected set ofwavelengths. Decision tree algorithms belong to the class of supervisedlearning algorithms. The aim of a decision tree is to induce aclassifier (a tree) from real-world example data. This tree can be usedto classify unseen examples (determine elements in a sample of unknowncomposition) which have not been used to derive the decision tree. Adecision tree is derived from training data. An example contains valuesfor the different attributes and what class the example belongs. In oneembodiment, the training data is spectral data from a number ofwavelengths across the training population (e.g. various elements)

In general there are a number of different decision tree algorithms,many of which are described in Duda, Pattern Classification, SecondEdition, 2001, John Wiley & Sons, Inc. Decision tree algorithms oftenrequire consideration of feature processing, impurity measure, stoppingcriterion, and pruning. Specific decision tree algorithms include, cutare not limited to classification and regression trees (CART),multivariate decision trees, ID3, and C4.5.

In one approach, when an exemplary embodiment of a decision tree isused, the spectral data for a representative number of wavelengthsacross a training population is standardized to have mean zero and unitvariance. The members (e.g. elements) of the training population arerandomly divided into a training set and a test set. For example, in oneembodiment, two thirds of the members of the training population areplaced in the training set and one third of the members of the trainingpopulation are placed in the test set. The spectral data for arepresentative number of wavelengths are used to construct the decisiontree. Then, the ability for the decision tree to correctly classifymembers in the test set is determined. In some embodiments, thiscomputation is performed several times for a given number ofwavelengths. In each iteration of the computation, the members of thetraining population are randomly assigned to the training set and thetest set. Then, the quality of the combination of traits is taken as theaverage of each such iteration of the decision tree computation.

In some embodiments, the spectral data across a representative number ofwavelengths is used to cluster a training set. For example, consider thecase in which ten wavelengths are used. Each member m (e.g. element) ofthe training population will have absorption orfluorescence/concentration values for each of the ten wavelengths. Suchvalues from a member m in the training population define the vector:

X_(1m)X_(2m)X_(3m)X_(4m)X_(5m)X_(6m)X_(7m)X_(8m)X_(9m)X_(10m)

where X_(im) is the fluorescence or absorbance/concentration of the ithwavelength in element m. If there are m elements in the training set,selection of i wavelengths will define m vectors. Those members of thetraining population that exhibit similar absorption orfluorescence/concentration curves across the training group will tend tocluster together. A particular combination of wavelengths of the presentinvention is considered to be a good classifier in this aspect of thepresent disclosure when the vectors cluster into the trait groups(elements) found in the training population. For instance, if thetraining population includes two different elements, a clusteringclassifier will cluster the population into two groups, with each groupuniquely representing either element.

Clustering is described on pages 211-256 of Duda and Hart, PatternClassification and Scene Analysis, 1973, John Wiley & Sons, Inc., NewYork. As described in Section 6.7 of Duda, the clustering problem isdescribed as one of finding natural groupings in a dataset. To identifynatural groupings, two issues are addressed. First, a way to measuresimilarity (or dissimilarity) between two samples is determined. Thismetric (similarity measure) is used to ensure that the samples in onecluster are more like one another than they are to samples in otherclusters. Second, a mechanism for partitioning the data into clustersusing the similarity measure is determined.

Similarity measures are discussed in Section 6.7 of Duda, where it isstated that one way to begin a clustering investigation is to define adistance function and to compute the matrix of distances between allpairs of samples in a dataset. If distance is a good measure ofsimilarity, then the distance between samples in the same cluster willbe significantly less than the distance between samples in differentclusters. However, as stated on page 215 of Duda, clustering does notrequire the use of a distance metric. For example, a nonmetricsimilarity function s(x, x′) can be used to compare two vectors x andx′. Conventionally, s(x, x′) is a symmetric function whose value islarge when x and x′ are somehow “similar”. An example of a nonmetricsimilarity function s(x, x′) is provided on page 216 of Duda.

Once a method for measuring “similarity” or “dissimilarity” betweenpoints in a dataset has been selected, clustering requires a criterionfunction that measures the clustering quality of any partition of thedata. Partitions of the data set that extremize the criterion functionare used to cluster the data. See page 217 of Duda. Criterion functionsare discussed in Section 6.8 of Duda.

More recently, Duda et al., Pattern Classification, 2nd edition, JohnWiley & Sons, Inc. New York, has been published. Pages 537-563 describeclustering in detail. More information on clustering techniques can befound in Kaufman and Rousseeuw, 1990, Finding Groups in Data: AnIntroduction to Cluster Analysis, Wiley, New York, N.Y.; Everitt, 1993,Cluster analysis (3d ed.), Wiley, New York, N.Y.; and Backer, 1995,Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, UpperSaddle River, N.J. Particular exemplary clustering techniques that canbe used in the present invention include, but are not limited to,hierarchical clustering (agglomerative clustering using nearest-neighboralgorithm, farthest-neighbor algorithm, the average linkage algorithm,the centroid algorithm, or the sum-of-squares algorithm), k-meansclustering, fuzzy k-means clustering algorithm, and Jarvis-Patrickclustering.

In some embodiments, the pattern recognition platform is based on PCA,as briefly described above. In such an approach, vectors for a selectedset of wavelengths can be selected in the same manner described forclustering above. In fact, the set of vectors, where each vectorrepresents spectral data for the select wavelengths from a particularmember (e.g. element) of the training populations, can be considered amatrix. In some embodiments, this matrix is represented in a Free-Wilsonmethod of qualitative binary description of monomers (Kubinyi, 1990, 3DQSAR in drug design theory methods and applications, Pergamon Press,Oxford, pp 589-638), and distributed in a maximally compressed spaceusing PCA so that the first principal component (PC) captures thelargest amount of variance information possible, the second principalcomponent (PC) captures the second largest amount of all varianceinformation, and so forth until all variance information in the matrixhas been accounted for.

Then, each of the vectors (where each vector represents a member of thetraining population) is plotted. Many different types of plots arepossible. In some embodiments, a one-dimensional plot is made. In thisone-dimensional plot, the value for the first principal component fromeach of the wavelengths is plotted. In this form of plot, theexpectation is that members of a first group (e.g. a first elementwithin the blood plasma) will cluster in one range of first principalcomponent values and members of a second group (e.g., a second elementwithin the blood plasma) will cluster in a second range of firstprincipal component values.

In one example, the training population comprises two groups: a firstelement and a second element. The first principal component is computedusing the spectral data for the select wavelengths of the presentdisclosure across the entire training population data set. Then, eachmember of the training set is plotted as a function of the value for thefirst principal component. In this example, those members of thetraining population in which the first principal component is positiveare the first element and those members of the training population inwhich the first principal component is negative are the second element.

In some embodiments, the members of the training population are plottedagainst more than one principal component. For example, in someembodiments, the members of the training population are plotted on atwo-dimensional plot in which the first dimension is the first principalcomponent and the second dimension is the second principal component. Insuch a two-dimensional plot, the expectation is that members of eachsubgroup represented in the training population will cluster intodiscrete groups. For example, a first cluster of members in thetwo-dimensional plot will represent a first element, a second cluster ofmembers in the two-dimensional plot will represent a second element, andso forth.

In some embodiments, the members of the training population are plottedagainst more than two principal components and a determination is madeas to whether the members of the training population are clustering intogroups that each uniquely represents a subgroup found in the trainingpopulation. In some embodiments, principal component analysis isperformed by using the R mva package (Anderson, 1973, Cluster Analysisfor applications, Academic Press, New York 1973; Gordon, Classification,Second Edition, Chapman and Hall, CRC, 1999.). Principal componentanalysis is further described in Duda, Pattern Classification, SecondEdition, 2001, John Wiley & Sons, Inc.

Nearest neighbor classifiers are another statistical method on which thepattern recognition platform can be based. Nearest neighbor classifiersare memory-based and require no model to be fit. Given a query point x0,the k training points x(r), r, . . . , k closest in distance to x0 areidentified and then the point x0 is classified using the k nearestneighbors. Ties can be broken at random. In some embodiments, Euclideandistance in feature space is used to determine distance as:

d(i)=∥x(i)−xo∥.

Typically, when the nearest neighbor algorithm is used, the expressiondata used to compute the linear discriminant is standardized to havemean zero and variance 1. In the present disclosure, the members of thetraining population are randomly divided into a training set and a testset. For example, in one embodiment, two thirds of the members of thetraining population are placed in the training set and one third of themembers of the training population are placed in the test set. Profilesrepresent the feature space into which members of the test set areplotted. Next, the ability of the training set to correctly characterizethe members of the test set is computed. In some embodiments, nearestneighbor computation is performed several times for a set number ofwavelengths. In each iteration of the computation, the members of thetraining population are randomly assigned to the training set and thetest set. Then, the quality of the spectral data for the set number ofwavelengths is taken as the average of each such iteration of thenearest neighbor computation.

The nearest neighbor rule can be refined to deal with issues of unequalclass priors, differential misclassification costs, and featureselection. Many of these refinements involve some form of weightedvoting for the neighbors. For more information on nearest neighboranalysis, see Duda, Pattern Classification, Second Edition, 2001, JohnWiley & Sons, Inc; and Hastie, 2001, The Elements of StatisticalLearning, Springer, N.Y.

The pattern classification and statistical techniques described aboveare merely examples of the types of models that can be used to constructa model for classification. It is to be understood that any statisticalmethod can be used in accordance with the present disclosure. Moreover,combinations of these described above also can be used. Further detailon other statistical methods and their implementation are described inU.S. patent application Ser. No. 11/134,688, incorporated by referenceherein in its entirety.

Computer Implementation

Other embodiments are within the scope and spirit of the invention. Forexample, due to the nature of software, functions described above can beimplemented using software, hardware, firmware, hardwiring, orcombinations of any of these. Features implementing functions can alsobe physically located at various positions, including being distributedsuch that portions of functions are implemented at different physicallocations. Steps of the invention may be performed using dedicatedmedical imaging hardware, general purpose computers, or both. As oneskilled in the art would recognize as necessary or best-suited forperformance of the methods of the invention, computer systems ormachines of the invention include one or more processors (e.g., acentral processing unit (CPU) a graphics processing unit (GPU) or both),a main memory and a static memory, which communicate with each other viaa bus. A computer device generally includes memory coupled to aprocessor and operable via an input/output device.

Exemplary input/output devices include a video display unit (e.g., aliquid crystal display (LCD) or a cathode ray tube (CRT)). Computersystems or machines according to the invention can also include analphanumeric input device (e.g., a keyboard), a cursor control device(e.g., a mouse), a disk drive unit, a signal generation device (e.g., aspeaker), a touchscreen, an accelerometer, a microphone, a cellularradio frequency antenna, and a network interface device, which can be,for example, a network interface card (NIC), Wi-Fi card, or cellularmodem.

Memory according to the invention can include a machine-readable mediumon which is stored one or more sets of instructions (e.g., software),data, or both embodying any one or more of the methodologies orfunctions described herein. The software may also reside, completely orat least partially, within the main memory and/or within the processorduring execution thereof by the computer system, the main memory and theprocessor also constituting machine-readable media. The software mayfurther be transmitted or received over a network via the networkinterface device.

While the machine-readable medium can in an exemplary embodiment be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more sets of instructions. The term “machine-readable medium”shall also be taken to include any medium that is capable of storing,encoding or carrying a set of instructions for execution by the machineand that cause the machine to perform any of the methodologies of thepresent invention. The term “machine-readable medium” shall accordinglybe taken to include, but not be limited to, solid-state memories (e.g.,subscriber identity module (SIM) card, secure digital card (SD card),micro SD card, or solid-state drive (SSD)), optical and magnetic media,and any other tangible storage media. Preferably, computer memory is atangible, non-transitory medium, such as any of the foregoing, and maybe operably coupled to a processor by a bus. Methods of the inventioninclude writing data to memory—i.e., physically transformingarrangements of particles in computer memory so that the transformedtangible medium represents the tangible physical objects—e.g., thearterial plaque in a patient's vessel.

As used herein, the word “or” means “and or or”, sometimes seen orreferred to as “and/or”, unless indicated otherwise.

As used in any embodiment herein, the term “module” may refer tosoftware, firmware and/or circuitry configured to perform any of theaforementioned operations. Software may be embodied as a softwarepackage, code, instructions, instruction sets and/or data recorded onnon-transitory computer readable storage medium. Firmware may beembodied as code, instructions or instruction sets and/or data that arehard-coded (e.g., nonvolatile) in memory devices.

“Circuitry”, as used in any embodiment herein, may comprise, forexample, singly or in any combination, hardwired circuitry, programmablecircuitry such as computer processors comprising one or more individualinstruction processing cores, state machine circuitry, and/or firmwarethat stores instructions executed by programmable circuitry. The modulesmay, collectively or individually, be embodied as circuitry that formspart of a larger system, for example, an integrated circuit (IC), systemon-chip (SoC), desktop computers, laptop computers, tablet computers,servers, smart phones, etc.

Any of the operations described herein may be implemented in a systemthat includes one or more storage mediums having stored thereon,individually or in combination, instructions that when executed by oneor more processors perform the methods. Here, the processor may include,for example, a server CPU, a mobile device CPU, and/or otherprogrammable circuitry. Also, it is intended that operations describedherein may be distributed across a plurality of physical devices, suchas processing structures at more than one different physical location.

The storage medium may include any type of tangible medium, for example,any type of disk including hard disks, floppy disks, optical disks,compact disk read-only memories (CD-ROMs), compact disk rewritables(CD-RWs), and magneto-optical disks, semiconductor devices such asread-only memories (ROMs), random access memories (RAMs) such as dynamicand static RAMs, erasable programmable read-only memories (EPROMs),electrically erasable programmable read-only memories (EEPROMs), flashmemories, Solid State Disks (SSDs), magnetic or optical cards, or anytype of media suitable for storing electronic instructions. Otherembodiments may be implemented as software modules executed by aprogrammable control device. The storage medium may be non-transitory.

As described herein, various embodiments may be implemented usinghardware elements, software elements, or any combination thereof.Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth.

Methods of Use

In certain aspects, the present invention is directed to methods ofproviding information regarding a medium. The method comprises providinga system comprising a light-emitting diode operating at a singlewavelength in a deep ultraviolet (UV) range for excitation of a targetin a medium and a plurality of semiconductor photodetectors. The systemmay be configured such that each semiconductor photodetector detectsonly a subset of emission from the excited target. A medium comprisingone or more target analytes may be exposed to at least a singlewavelength in the deep UV spectrum from the light-emitting diode of thesystem to thereby excite the target analyte in the medium. The methodmay further comprise detecting emission from the excited one or moretarget analytes via the plurality of semiconductor photodetectors of thesystem to thereby produce emission data and processing the emissiondata, thereby providing information regarding the medium.

In certain embodiments, the medium may be selected from the groupconsisting of a biofluid, water, an aluminum surface, a stainless steelsurface, a granite surface, a ceramic surface, a plastic surface, and ametallic surface. In an embodiment, the target analyte may be selectedfrom the group consisting of a microorganism, a biomolecule, and achemical. In a preferred embodiment, the medium is water and the targetanalyte is one or more pathogens.

In an embodiment, processing the emission data may comprise identifyingpresence of one or more target analytes in the medium. Processing theemission data may further comprise identifying the one or more targetanalytes in the medium. Processing the emission data may furthercomprise quantifying the one or more target analytes in the medium.

In certain embodiments, the method may further comprise displaying on agraphical user interface results of the processing step.

Moreover, certain embodiments of the invention use emission data todetermine total microbial load and bioburden measurements. The presentinvention comprises directing one or more wavelengths of light that areeach within a deep ultraviolet (UV) spectrum into a medium comprising abiological substance to thereby excite the biological substance in themedium. Emission is detected from the excited biological substance viaone or more semiconductor photodetectors, thereby producing deep UVemission data. The deep UV emission data is analyzed for presence of adeep UV spectral signature indicative of the biological substance,wherein presence of the deep UV spectral signature indicates that themedium comprises a biological substance. While excitation may be in thedeep UV region, emission may be in the UV region, such as in the UVA andUVB regions.

The emission data may be used to determine total microbial load.Microbial load is the number and type of microorganisms contaminating anobject or organism, such as non-specific biological and microbiologicalcontamination. Total microbial load indicates the microbiology presentin the sample. Emission data may be analyzed for deep UV spectralsignatures indicative of microbiology. Emission data may be analyzed fordeep UV spectral signatures indicative of presence and quantity ofmicrobiology. For example, analyzing may include comparing the UVspectral signature with a library of UV spectral signatures of varyingamounts and types of microbiology on or in a variety of media. Systemsof the invention may indicate the total microbial load in the sampleafter detecting the UV spectral signatures indicative of microbiology.

The emission data may be used to determine bioburden, or the number ofbacteria living on a surface or within a liquid. Often, bioburden refersto the number of microorganisms on an unsterilized surface. Emissiondata may be analyzed for deep UV spectral signatures indicative ofpresence and quantity of microorganisms. For example, analyzing mayinclude comparing the UV spectral signature with a library of UVspectral signatures of varying amounts and types of microorganisms on orin a variety of media. Systems of the invention may indicate thebioburden in the sample after detecting the UV spectral signaturesindicative of the presence or quantity of microorganisms.

In some embodiments, the invention provides different detector andfilter configurations for detection using the target thresholds. Forexample, a first configuration uses a single detector to determine thepresence or absence of a target, such as a non-specific contamination.Such a single-detector embodiment may encompass a portable detector,such as a detector used while traveling. Another configuration uses twodetectors to determine and distinguish between the presence ofmicrobiology and general biology targets. To specify certain strains oftargets in a mixture or sample, a configuration of the invention usesseven detectors. Another configuration uses seven or more detectors todistinguish between dead and live specified strains of targets.

As a non-limiting example, the target may be selected from bacteria,fungi, protein, a cell, a virus, a nucleic acid, a receptor, a ligand, ahormone, a drug, a chemical substance, or any molecule known in the art.For example, the target may be selected from Benzimidazole, 1-Naphthol,Carbofuran, Bisphenol A, Carbaryl-d7, Naphthalene, p-xylene, Tryptophan,Phenanthrene, Tyrosine, ethylestradiol, Propoxur, Ibuprofen,Beta-estradiol, Dimethyl phthalate, Chlopyrifos, Ethylbenzene, Dibutylphthalate, Benzo[a]pyrene, Benzene, Biphenyl,3,5,6-Trichloro-2-pyridinol, Bisphenol S, Imidazole, hydrocortisone,Toluene, Alachlor, Atrazine, QB3 Tap water, Di n octyl phthalate,Acetaminophen, Estrone, Glyphosate, Lead, Bis(2-ethylhexyl) phthalate,clarithromycin, Trihalomethane, diisodecylphthalate. phenylalanine,Heptachlor, testosterone, dieldrin, Tozaphenel, Aldrin, DTT, cortisol,and Endrin.

In certain embodiments, the target is a pathogen, or pathogenic bacteriaor fungi. A pathogen is a biological agent, such as a microorganism(e.g. bacterium or protozoan), that causes disease or illness to itshost. In other embodiments, the target is a gram positive or gramnegative bacteria.

Exemplary fungal species include species from the Candida genus,Aspergillus genus, and Cryptococcus genus. In particular embodiments,the specific fungal species include C. albicans, C. glabarata, C.parapsilosis, C. tropicalis, C. krusei, Cryptococcus neoformans, andCryptococcus gattii.

Exemplary bacteria include bacteria of the Escherichia genus, Listeriagenus, Clostridium genus, Enterobacteriaceae family, Mycobacteriumgenus, Shigella genus, Borrelia genus, Campylobacter genus, Bacillusgenus, Salmonella genus, Enterococcus genus, Streptococcus genus (suchas Pneumococcus), Acinetobacter genus, Strenotrophomonas genus,Pseudomonas genus, Neisseria genus, and Haemophilus genus, and acombination thereof. The method may also be used to detect the mecAgene, which is a bacterial gene associated with antibiotic resistance.

Pathogen Detection and Identification

The present inventions allow for pathogen detection and identification.Available technologies merely provide an indication of yes or no as towhether there is biological material present. With the presentinvention, bandpass configuration allows for separation of microbiologyfrom each other and from amino acid signatures. For example, theexcitation used in six channel detection can separate out differentmicrobiological strains from each other, as well as from raw amino acidsignatures. The ability to separate the bacteria signature without anamino acid is possible due to separation of the channels.

The unique signature associated with three pathogens was used in theinvention. By combining known amino acids with pathogen signatures, aconfiguration was determined for detection at ranges where there are nooverlapping parts (FIG. 18). As shown in FIG. 18, when comparing theexcitation of Pathogen 1, Pathogen 2, and Pathogen 3, there wereoverlapping regions and non-overlapping regions. For example, theoverlapping regions were observed at particular excitations. Thenon-overlapping regions indicated the unique signature for eachpathogen. By using separate channels within the deep UV range, emissionmay be detected that differentiates bacteria and other pathogens fromone another, thereby allowing for identification of the bacteria. If thesignals could not be separated, the bacteria and other pathogens couldnot be distinguished and there would only be an indication of whether ornot a biological substance was present in the sample.

To separate the microbiology targets, Mahalanobis Distance (see FIG. 31)was implemented using the bacterial and amino acid signaturesemission-excitation matrix (EEM) spectra. The bacterial signatures andamino acid signatures are shown in FIG. 31. The EEM signatures werenormalized and binned into six channels. The Mahalanobis Distanceanalysis looked for the largest difference between the bacterial andamino acids. The wavelengths indicated separated the microbiology fromthe amino acid signatures.

As an example, the present invention may be used to test for presence ofStaphylococcus Aureus in tap water. A user may input a source of themedium being tested, e.g. tap water. The sample (e.g. tap water) wouldthen be scanned by the apparatus of the present invention. Results wouldbe processed by comparing the spectra from the scanned tap water sampleto a database of known spectra from known sources. If the spectrum forthe sample is within the threshold for a known source, then the mediumquality (e.g. the water quality of the tap water) is output. However, ifthe sample is not within the threshold, then the spectrum for the sampleis compared to a database of contaminants. For example, the database ofcontaminants includes lab-produced samples of varying concentrations forvarious bacterial and chemical components within various sources.

If the spectrum for the sample is within a threshold for a contaminationsource from the contaminant database, then the contamination may beidentified. For instance, the tap water sample may match up with adatabase entry of a known bacterial contamination, e.g. StaphylococcusAureus. The tap water sample would then be identified as having acontaminant which is Staphylococcus Aureus.

The contamination may be further quantified based on calculations fromconcentration studies of the contaminants. For example, theconcentration of Staphylococcus Aureus in the tap water sample would bedetermined based on concentration studies of Staphylococcus Aureus insamples from tap water. The results of the scan in the tap water examplewould indicate the presence of Staphylococcus Aureus as a contaminantand the concentration of Staphylococcus Aureus in the tap water sample.

Water analysis

The systems and methods of the invention are applicable to manydifferent types of media and surfaces, as already mentioned throughoutthis application. A particular area of interest is water analysis andwater quality. In that manner, the present invention provides a range oftargets that can be detected within different water sources and watertypes, such as tap, bottle, and well water and on aluminum and stainlesssteel surfaces. Target contaminants may be selected from the groupconsisting of pathogens, amino acids, hormones, industrial chemicals,pharmaceuticals, and biomarkers. In an embodiment, urine and salivamatrices may be analyzed for the human biomarker analysis. In certainembodiments, for a deeper water analysis, users may request a collectionkit and EPA certified facilities will email them a full, easy tounderstand, report. Quantification according to the present inventionmay positively impact the creation of cleaner rivers and water sources.

For example, devices of the invention may be used for monitoring anddetection of water quality in industrial and manufacturing processes.Devices of the invention may be used for water quality detection inwater kiosks that provide sale of tap water, such as in developingcountries. Utilities providers may use devices of the invention toensure water quality being provided to customers. Similarly, buildingowners may use devices of the invention for monitoring and detection ofwater quality within a building, such as to ensure safe water fortenants. Devices of the invention may also be used in the food andbeverage industry, pharmaceutical industry, and healthcare industry.

There is bacterial contamination in tap water. If the water was purewater, no fluorescence would show when doing an EEM (FIG. 25). Tap waterhas a residual signature in region that is linked to decomposingbiological substances. However, pathogens such as bacteria and variousother targets fluorescence in the region marked by the circle (FIG. 26).

For example, the present invention may be used to test water quality. Auser may input a source of the water being tested, e.g. bottled water,tap water from a particular location, or well water. The water sample(e.g. bottled water) would then be scanned by the apparatus of thepresent invention. Results would be processed by comparing the spectrafrom the scanned water sample to a database of known spectra from knownsources. If the spectrum for the sample is within the threshold for aknown source, then the water quality is output. However, if the sampleis not within the threshold, then the spectrum for the sample iscompared to a database of contaminants. For example, the database ofcontaminants includes lab-produced samples of varying concentrations forvarious bacterial and chemical components within various sources.

If the spectrum for the sample is within a threshold for a contaminationsource from the contaminant database, then the contamination may beidentified. For instance, the bottled water sample may match up with adatabase entry of a known E. coli contamination in bottled water. Thewater sample would then be identified as having a contaminant which isE. coli .

The contamination may be further quantified based on calculations fromconcentration studies of the contaminants. For example, theconcentration of E. coli in the water bottle sample would be determinedbased on concentration studies of E. coli in water samples from waterbottles. The results of the scan in the water bottle example wouldindicate the presence of E. coli as a contaminant and the concentrationof E. coli in the water bottle sample.

Food

The present invention may help to shift global agricultural land tobeing more than 1% certified organic. By using detection of the presentinvention, safe and environmental practices may flourish. Awareness maydrive markets to efficiency and innovation, and new markets may becreated for other technologies.

In an example, in consumer signaling, consumers may be empowered bymolecular insight into their food and water. The consumer interest mayincentivize retailers to invest in detection technology for the presentinvention. Retailers may drive adoption throughout the food and watersupply chain such as through the distributor, processor/supplier, orgrower/water source.

In an embodiment, the spectral database of the present invention may bevaluable to retailers wanting to gain customer confidence. In anembodiment, the present invention may become the standard in molecularscanning. In an embodiment, the present invention may createstore-specific scanning technology. In an embodiment, a databaseaccording to the present invention may be accessed for a monthly oryearly fee. In an embodiment, the present invention may monetizethroughout the supply chain on the back of consumer knowledge and demandfor cleaner products. Retail stores may rely on such a clean supplychain reputation and may be incentivized to integrate the presentinvention in stores and throughout suppliers.

Fruit and vegetable pesticide scans are shown in FIG. 27.

For example, the present invention may be used to test for presence of apesticide on a fruit or vegetable. A user may input a source of themedium being tested, e.g. a fruit or vegetable. The sample (e.g. anapple) would then be scanned by the apparatus of the present invention.Results would be processed by comparing the spectra from the scannedapple sample to a database of known spectra from known sources. If thespectrum for the sample is within the threshold for a known source, thenthe medium quality is output. However, if the sample is not within thethreshold, then the spectrum for the sample is compared to a database ofcontaminants. For example, the database of contaminants includeslab-produced samples of varying concentrations for various bacterial andchemical components within various sources.

If the spectrum for the sample is within a threshold for a contaminationsource from the contaminant database, then the contamination may beidentified. For instance, the apple sample may match up with a databaseentry of a known pesticide contamination, e.g. flazasulfuron. The applesample would then be identified as having a contaminant which isflazasulfuron.

The contamination may be further quantified based on calculations fromconcentration studies of the contaminants. For example, theconcentration of flazasulfuron in the apple sample would be determinedbased on concentration studies of flazasulfuron in similar samples fromapples. The results of the scan in the apple example would indicate thepresence of flazasulfuron as a contaminant and the concentration offlazasulfuron in the apple sample.

Embodiments of the invention may be used to detect contaminants onfoods. As an example, the invention may be used to detect pesticides onfruits and vegetables. For example, the invention may be used to detecta pesticide on a kale leaf. As an example, the invention may be used todetect a bacterial increase on the surface of meats and fish todetermine freshness or lack thereof.

In an embodiment, the invention may be used to monitor the process washwater quality for fresh produce cleaning, such as that completed by foodproducers and suppliers, can give early indications of contaminationevents. For instance, tested sources may include well water directlyfrom a tank, well water at tap, a first wash with biocide, direct runofffrom produce, waste water at the end of a process line, or water fromplastic crate wash. As another example, the invention can be used byindividuals who wash a head of romaine lettuce and then scan thecaptured water to get an indication of sanity level or safety level ofthe romaine lettuce.

Healthcare

The present invention may help to reduce hospital-acquired infections.Each year, 1.7 million patients are infected by hospitals. A staggering1 in 25 patients resulted in approximately 99,000 deaths unrelated toconditions for which the patients were admitted to the hospitals.Hospital-acquired infections in the United States result in $38 billionin extra costs each year. This needless contamination may be due tounsterilized surfaces in the hospitals. With the present invention,target analytes may be detected on surfaces such as stainless steel andaluminum. These surfaces are prevalent in hospital settings.

For example, the present invention may be used to test for presence ofStaphylococcus Aureus on surfaces in a hospital. A user may input asource of the medium being tested, e.g. an aluminum surface. The sample(e.g. an aluminum surface) would then be scanned by the apparatus of thepresent invention. Results would be processed by comparing the spectrafrom the scanned aluminum surface sample to a database of known spectrafrom known sources. If the spectrum for the sample is within thethreshold for a known source, then the medium quality is output.However, if the sample is not within the threshold, then the spectrumfor the sample is compared to a database of contaminants. For example,the database of contaminants includes lab-produced samples of varyingconcentrations for various bacterial and chemical components withinvarious sources.

If the spectrum for the sample is within a threshold for a contaminationsource from the contaminant database, then the contamination may beidentified. For instance, the aluminum surface sample may match up witha database entry of a known bacterial contamination, e.g. StaphylococcusAureus. The aluminum surface sample would then be identified as having acontaminant which is Staphylococcus Aureus.

The contamination may be further quantified based on calculations fromconcentration studies of the contaminants. For example, theconcentration of Staphylococcus Aureus in the aluminum surface samplewould be determined based on concentration studies of StaphylococcusAureus in samples from aluminum surfaces. The results of the scan in thealuminum surface example would indicate the presence of StaphylococcusAureus as a contaminant and the concentration of Staphylococcus Aureusin the aluminum surface sample.

Blockchain

In certain embodiments, blockchain technology may be used. Blockchain isa digital, decentralized transaction and data management technology,such as described in Yli-Huumo et al, Where Is Current Research onBlockchain Technology?—A Systematic Review, PLOS ONE, 2016, incorporatedherein. Data integrity and authentication are essential issues in theBlockchain environment. It is necessary that when data gets sent andverified, it has not been altered or tampered with. A private key may bean authentication element. A smart phone may be used as a secondauthentication factor. By using blockchain in the present invention,sample data may be shared with the database and tampering of that sampledata may be avoided.

The use of blockchain may be directed to quantified water, quantifiedfood & agriculture, quantified environment, and quantified health-homediagnostics. In the ecosystem according to the present invention,product, data, and people interact (FIG. 28). Further, cloud andblockchain provide an intelligent spectral database of key environmentaland human health markers. The value and utility of the library ofbiological/molecular ‘signatures’ increases with every scan by a user.The community of consumers is also important, as users are incentivizedto discover the molecular universe that surrounds them. These consumersallow for gaining of insight into their personal and environmentalhealth while strengthening the product-data performance of the presentinvention.

In certain embodiments, data may be secured on the blockchain, which maybe the first global intelligent database of key environmental and humanhealth markers (FIG. 29). Users may choose what gets written to theblockchain (using a RES token). Scanned data is private and hashed bydefault. Data is immutable and cannot be erased or altered by anyone. Toview data a “View key” is needed that only the user will have. All datamay be geotagged and time-stamped. Users may earn RES tokens on data ifwritten to the blockchain and made available for analysis (pending SECrules).

In an embodiment, time-stamped, geo-tagged, and encrypted data may bewritten to the blockchain. Monitoring devices according to the presentinvention may be stationed before and after point-source polluters (FIG.30). This will establish a guardian network of real-time, verified datathat cannot be manipulated or deleted.

In an embodiment, consumers may scan whole foods for contaminants.Consumers may scan bio-fluids for health markers. In an embodiment,government agencies may monitor public waterways. In an embodiment,supermarkets may monitor growers and suppliers. In an embodiment, foodand beverage processors may scan water and surfaces. In an embodiment,hospitals may scan facilities.

Atmospheric Conditions

In particular embodiments, the present invention may be used indifferent atmospheric conditions. Preferably the invention is used underEarth's atmospheric conditions. The invention may be used under otheratmospheric conditions. As non-limiting examples, the present inventionmay be used for detection of target analytes in media on a spacestation, a rocket, on Mars, or under water.

Detection of Gram+/ Gram−

In certain embodiments, the invention is used to tell the differencebetween Gram/+ and Gram/− bacteria. In a non-limiting study, theinvention was used on six bacteria strains. The gram+and gram- bacteriaspecies used in a non-limiting study include Listeria welshimeri,Listeria seeligeri, Staphylococcus epidermidis, Klebsiella aerogenes,Pseudomonas putida, Enterobacter, and Escherichia coli. FIG. 32 showsthe clustering of spectra of the gram+ and gram− bacteria species.

EXAMPLES Example 1

FIG. 1 shows the scanner or detector according to the present invention.The device is small and portable. The device includes an indicator,which may be an OLED display. The device also includes the lens anddetectors, as well as a scan button. Optionally, there may be a microgrip texture on the device.

Example 2

FIG. 2 shows side and top views of the OLED display scanner or detectoraccording to the present application. The display may be an E-inkdisplay in certain embodiments. The display screen may be the userinterface for the device, with all components of the system included inthe detector device. The device is small and portable. The device isalso user-friendly, as evidenced by the simple scanning button locatedon a side of the device.

Example 3

FIG. 3 shows the dimensions of the scanner or detector. The top of thedevice, or the display screen, may measure 76 mm in diameter. Thescanning side of the device, or the side containing the detector andlens, may measure 45 mm in diameter. The device may have a thickness of35 mm.

Example 4

FIG. 4 shows the display for results of the sample. The display screenmay have simple, easy-to-read features. For example, a check may meanthat the sample is cleared, while an X may mean that the sample iscontaminated.

Example 5

FIG. 5 shows the user interface on an external source. The userinterface may be on a smartphone. Such an embodiment differs from havingthe user interface integrated in the detector device itself. The userinterface may also be on other suitable means, such as an externallaptop computer or tablet.

Example 6

In certain embodiments, the detector of the present invention may beused in a minilab setup. As shown in FIG. 6, the minilab may have ahousing, a detector, and a sample slide plate. As shown in FIG. 7, theminilab may have a housing, a detector, and a sample cup. The sample maybe placed on the sample slide plate or sample cup and then inserted intothe minilab housing. The detector may be fitted within the housing tostabilize the detector while scanning.

Example 7

In certain embodiments, the detector of the present invention may beused in a wedge setup. As shown in FIG. 8, the wedge may come indifferent angles to fit any surface. The wedge may use cases (sink,toilet, shelf, wall). The back side of the wedge may include a VHB tapelayer to strongly stick to any type of surface.

Example 8

In certain embodiments, systems of the invention comprise a housing thathas multiple components, and one of the components is a base or tripod.In such an embodiment, the detector of the present invention may be usedin a tripod setup. As shown in FIG. 9, the detector may be used fordetection of a sample in a small sample cup. As shown in FIG. 10, thedetector may be used to for detection of a large sample. The tripodsetup allows for the detector to be stabilized while scanning.

Example 9

As shown in FIG. 11, the detector may be used as a toilet adapter. Thetoilet adapter system may be used to analyze urine within the toiletbowl water. For example, the adapter may detect anomalies or diseaseswithin the urine. Measurements may include high protein content andurinary tract infections (UTI). In addition, the device may be used asan early warning system for diseases and health markers. For example,the device may be used for disease detection, as certain cancers mayshift the spectral signature of urine in the UV region. As anotherexample, the device may be used to detect hormones from ovulation cyclesand pregnancy markers, such as hCG. Users may be notified of thedetection results by smartphone notification.

Example 10

As shown in FIG. 12, the detector may be used a sink adapter. Thisallows users access to the water quality within their environment, suchas in a home, hospital setting, or work environment. People use sinksfrequently throughout the day. If a contaminant is present in the watersource for the sink, there may be increased safety concerns. Byinstalling the detector on the plumbing leading to the faucet of a sink,users have the opportunity to detect the water source before using thewater in the sink. Further, users may opt to obtain sample data morefrequently or set the detector to scan the water source at specifiedtime periods to monitor the water quality.

Example 11

FIG. 13 shows an embodiment of an on-line detector. The on-line detectoris useful for any suitable detection of any water supply source. Forexample, in an embodiment, the on-line detector is coupled to a watersource, such as a pipe. In some cases, the detector is mounted to a pipeattachment, such as a 2 inch pipe attachment or a T-junction pipefitting. The device provides detection of the liquid inside the pipe. Insome examples, similar to Example 10, the device is coupled to a pipefor a sink and detects bioburden or total microbial load in the liquidleading to the faucet of the sink. The on-line embodiment provides a lowprofile with a detection window for non-disruption mounting andun-mounting of the device.

Example 12

FIG. 14 shows an embodiment of an in-line detector probe. As shown inFIG. 14, the probe is inserted into the pipe, with part of the probedisposed within the pipe, for detection of bioburden or total microbialload in the liquid flowing through the pipe. The in-line detector probeis useful for any suitable detection of any water supply source. In onenon-limiting example, the probe may be used in industrial settings tomonitor water quality, particularly where a sink or end-point to a lineis unavailable or if water quality should be monitored in a particularclosed section of a manufacturing process. In another non-limitingexample, the probe may be used in a building, such as a hotel, tomonitor water quality provided in the building.

Example 13

FIG. 15 shows an embodiment of an off-line, stand-off detector, such asa tap attachment. The device monitors water quality from a tap mount onthe line, but the device itself is off-line, or off of the pipe. Theoff-line, stand-off detector is useful for any suitable detection of anywater supply source. Providing the device off-line as a stand-offdetector is non-invasive and eliminates detector biofouling and allowshigh accuracy sample readings. Having a tap mount that does not contactthe water gives the end user the nearest to a pure result as possible atthe point-of-use and point-of-care. In one non-limiting example, theoff-line, stand-off detector is used as a tap attachment in aresidential setting. In another non-limiting example, the detector isused as a tap attachment in a water kiosk.

Example 14

The invention uses deep UV autofluorescence to detect and identifyvarious strains of bacteria. Bacteria cells have unique autofluorescencesignatures when excited in the UV region (see Label-Free bacterialimaging with Deep-UV-laser induced native fluorescence, Bhartia, Salas,Hug, Reid, A. Lane, Edwards, Nealson, 2010). Excitation Emission Matrix(EEM) has been proposed as a potential tool for water monitoring, butEEM only makes inferences (see Fluorescence as a potential monitoringtool for recycled water systems: A review, Henderson et al. 2009).

Real-World Example of Contaminated Tap Water

Pure water would show no fluorescence signature when doing an EEM.However, bacteria and various other contaminants fluoresce in the regionmarked by the blue circle. Some tap waters show a residual signature(green perforated circle) in a region that is linked to dissolvedorganic matter (DOM). This sample was taken from a restaurant in MarinCounty, Calif. USA. FIG. 33 shows clean, distilled water, while FIG. 34shows contaminated restaurant water.

Discovered Unique Signatures Between Different Biologies

Fluorescence profiles of the cells are due to the amino acidssignatures. Not only is there a difference in amino acid composition foreach strain, but they are most likely dynamically changing depending onlife-cycle of the cell (alive>growing>dying>dead). FIG. 35 showsfluorescence signatures of amino acids and microbiology.

AI Has Advanced Native-Induced Fluorescence Technology To IdentifyMicrobiology

Fluorescence signatures from bacteria are mainly thought to be fromtryptophan; however, we have found a stark difference between thesignatures for E. coli, Salmonella, Staphylococcus aureus, Listeriamonocytogenes and other dissolved organic matter (DOM). FIG. 36 comparescurrent technology to advancements of the invention (Orb).

Ability to quantify

Correlation data has been geared towards testing the invention'sclassification and quantification algorithm against known enumeratedbacteria. To date, the invention has >98% accuracy when identifyingbacteria (E. coli, Salmonella, Staphylococcus aureus, Listeria) in tapwater that contained other fluorescing biological contaminants tochallenge the system. FIG. 37 shows a concentration curve.

Deconvolution Study: Specifying Individual Bacteria From a Multi-SpeciesMixture

With the invention's library of bacteria signatures, it is possible tobreak down individual bacterial species in a mixture and predict thepresence and quantity of each. FIG. 38 shows deconvolution of abacterial mixture. FIG. 39 shows a table of R² predicted vs. actualcomposition.

Deconvolution Study: Dead vs Live Bacteria

The invention can accurately differentiate and determine the quantity ofviable bacteria in a mixture of dead vs. live cells. FIG. 40 shows thespectral profile for E. coli when viable (live) and confirmed non-viable(dead) after autoclaving. FIG. 41 shows the emission center wavelengthfor various bacterial species when viable (live) and confirmednon-viable (dead) after autoclaving. FIG. 42 shows a table of R²predicted vs actual viability.

Cross Validation Study: Food Process Wash Water

An aim of the invention was to predict the presence/absence of E. colifrom samples collected from various waters in one of North America'slargest fresh produce processing plants. The invention's algorithmclassified the samples in relation to the fluorescence database we havecurated from measuring known pathogens in our facility. The highlightedregion was the only discrepancy of the invention (Orb) detection vs EPAapproved method for detection of E. coli. This could be due to thesample containing bacteria that were non-culturable. FIG. 43 showsdifferent sources for detection using the invention (Orb) and the EPAapproved method (coliform/E. coli).

Total Microbial Load (TML) can be the real-time monitoring indicator ofwater safety complimenting the randomized spot-check E. coli or Coliformtest

WHO and EPA waterborne disease initial screening methods do not detectnon-coliform or protozoan pathogens such as: Salmonella,Cryptosporidium, Giardia, Listeria etc. Orb can detect all microbiologypresent in a given sample—even if we can't specify them—giving insightsnormally never detected and adding a complimentary layer of intelligenceto current methods such as when to actually take a coliform test. TheGold Standard failed to detect pathogens after 24 hours, while theinvention (Orb) detects in seconds, e.g. 3 seconds. FIG. 44 shows anoutline of a test method of the invention where a source was doped withsalmonella, the invention was used to detect contamination, and theapproved Gold Standard EPA method was used to detect contamination. FIG.45 shows results of the comparison of detection using the invention(Orb) to the Gold Standard detection.

Target Library Continually Grows

FIG. 46 shows a selection of detection capabilities to date. Moreover,the invention provides additional capabilities of surface scanning, foodscanning, and bio-fluid scanning. In surface scanning, the invention isused to scan stainless steel and aluminum surfaces for pathogenmonitoring and cleanliness proof statements. In food scanning, theinvention is used to monitor food contamination from pathogens andselect chemicals. In bio-fluid scanning, the invention is used tomonitor urine for protein levels and infection.

Incorporation by Reference

References and citations to other documents, such as patents, patentapplications, patent publications, journals, books, papers, webcontents, have been made throughout this disclosure. All such documentsare hereby incorporated herein by reference in their entirety for allpurposes.

Equivalents

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The foregoingembodiments are therefore to be considered in all respects illustrativerather than limiting on the invention described herein. Scope of theinvention is thus indicated by the appended claims rather than by theforegoing description, and all changes which come within the meaning andrange of equivalency of the claims are therefore intended to be embracedtherein.

What is claimed is:
 1. A system for detecting a target in a mediumcomprising: a light-emitting diode operating at a single wavelength in adeep ultraviolet (UV) range for excitation of a target in a medium; anda plurality of semiconductor photodetectors; wherein the system isconfigured such that each semiconductor photodetector detects only asubset of emission from the excited target.
 2. The system of claim 1,wherein the emission is in a detection range of 300-400 nm.
 3. Thesystem of claim 1, wherein the system configuration for eachsemiconductor photodetector detecting only a subset of emission from theexcited target comprises each semiconductor photodetector having adifferent filter applied thereto or a grating element to split theemission from the excited target such that each semiconductorphotodetector detecting only a subset of emission from the excitedtarget.
 4. The system of claim 1, wherein the system comprises at leastsix semiconductor photodetectors.
 5. The system of claim 1, wherein theplurality of semiconductor photodetectors are avalanche photodiodedetectors or silicon sensors.
 6. The system of claim 1, furthercomprising a processor configured to process data received from theplurality of semiconductor photodetectors.
 7. The system of claim 6,wherein the processor is integrated into the system.
 8. The system ofclaim 6, wherein the processor is remote from the system.
 9. The systemof claim 6, wherein the processor is a computer, smart phone, ormicrocontroller.
 10. The system of claim 1, wherein the system is aportable, handheld, point-and-shoot system.
 11. A method of providinginformation regarding a medium, the method comprising: providing asystem comprising a light-emitting diode operating at a singlewavelength in a deep ultraviolet (UV) range for excitation of a targetin a medium; and a plurality of semiconductor photodetectors; whereinthe system is configured such that each semiconductor photodetectordetects only a subset of emission from the excited target; exposing amedium comprising one or more target analytes to at least a singlewavelength in the deep UV spectrum from the light-emitting diode of thesystem to thereby excite the target analyte in the medium; detectingemission from the excited one or more target analytes via the pluralityof semiconductor photodetectors of the system to thereby produceemission data; and processing the emission data, thereby providinginformation regarding the medium.
 12. The method of claim 11, whereinthe medium is selected from the group consisting of a biofluid, water,an aluminum surface, a stainless steel surface, a granite surface, aceramic surface, a plastic surface, and a metallic surface.
 13. Themethod of claim 11, wherein the target analyte is selected from thegroup consisting of a microorganism, a biomolecule, and a chemical. 14.The method of claim 11, wherein the medium is water and the targetanalyte is one or more pathogens.
 15. The method of claim 11, whereinthe method is performed in Earth's atmospheric conditions.
 16. Themethod of claim 11, wherein the method is performed outside of Earth'satmospheric conditions.
 17. The method of claim 11, wherein processingthe emission data comprises identifying presence of one or more targetanalytes in the medium.
 18. The method of claim 17, wherein processingthe emission data further comprises identifying the one or more targetanalytes in the medium.
 19. The method of claim 18, wherein processingthe emission data further comprises quantifying the one or more targetanalytes in the medium.
 20. The method of claim 11, further comprisingdisplaying on a graphical user interface results of the processing step.21. A system for analyzing a sample medium, the system comprising aprocessor coupled to a non-transitory memory configured to cause thesystem to: receive sample data associated with a sample medium, whereinthe sample data comprises identification of a source of the samplemedium and spectral data of the sample medium comprising one or moreanalytes; compare the sample data to a reference dataset comprising aplurality of reference spectra, wherein each of the plurality ofreference spectra comprises a spectral profile associated with anidentified medium that comprises an identified level of one or moreidentified analytes in the identified medium; and determine whether thesample data matches one of the plurality of reference spectra.
 22. Thesystem of claim 21, wherein if the processor determines that the sampledata matches one of the plurality of reference spectra, the processor isfurther configured to: generate a sample medium quality score for thesample medium based on the identification of the one or more analytes inthe sample medium and a level of the one or more analytes in the samplemedium.
 23. The system of claim 22, wherein the processor is furtherconfigured to: output the sample medium quality score to a userinterface.
 24. The system of claim 23, wherein the user interface isintegrated into the system comprising the processor.
 25. The system ofclaim 23, wherein the user interface is remote from the systemcomprising the processor.
 26. The system of claim 21, wherein if theprocessor determines that the sample data does not match any of theplurality of reference spectra in the reference dataset, the processoris further configured to: compare the sample data to the referencespectra in the reference dataset for an identified contaminant in one ormore of the reference spectra; and determine whether the sample datamatches an identified contaminant in one or more of the plurality ofreference spectra, wherein one or more matches identifies one or morecontaminants in the sample medium.
 27. The system of claim 26, whereinthe processor is further configured to: quantify an amount of at leastone of the one or more contaminants in the sample medium.
 28. The systemof claim 27, wherein the processor is further configured to: output anidentification and quantification of the one or more contaminants in thesample medium to a user interface.
 29. The system of claim 28, whereinprocessor is further configured to: output the sample medium qualityscore to a user interface.
 30. The system of claim 29, wherein the userinterface is integrated into the system comprising the processor. 31.The system of claim 29, wherein the user interface is remote from thesystem comprising the processor.
 32. The system of claim 21, wherein thespectral data of the sample medium comprising one or more analytes isdeep ultraviolet (UV) spectral data and each of the first plurality offirst reference spectra is deep ultraviolet (UV) reference spectra. 33.A system for analyzing a sample, the system comprising: an excitationsource for exciting a sample medium comprising one or more analytes; adetector for receiving spectral data of the sample medium comprising theone or more analytes; and a processor operably associated with thesystem, the processor being coupled to a non-transitory memoryconfigured to cause the system to: receive sample data associated withthe sample medium, wherein the sample data comprises identification of asource of the sample medium and the spectral data of the sample mediumcomprising the one or more analytes; compare the sample data to areference dataset comprising a plurality of reference spectra, whereineach of the plurality of reference spectra comprises a spectral profileassociated with an identified medium that comprises an identified levelof one or more identified analytes in the identified medium; anddetermine whether the sample data matches one of the plurality ofreference spectra.
 34. The system of claim 33, wherein if the processordetermines that the sample data matches one of the plurality ofreference spectra, the processor is further configured to: generate asample medium quality score for the sample medium based on theidentification of the one or more analytes in the sample medium and alevel of the one or more analytes in the sample medium.
 35. The systemof claim 34, wherein the processor is further configured to: output thesample medium quality score to a user interface.
 36. The system of claim35, wherein the processor and the user interface are integrated into thesystem.
 37. The system of claim 35, wherein the processor and/or theuser interface are remote from the system and/or each other.
 38. Thesystem of claim 33, wherein if the processor determines that the sampledata does not match any of the plurality of reference spectra in thereference dataset, the processor is further configured to: compare thesample data to the reference spectra in the reference dataset for anidentified contaminant in one or more of the reference spectra; anddetermine whether the sample data matches an identified contaminant inone or more of the plurality of reference spectra, wherein one or morematches identifies one or more contaminants in the sample medium. 39.The system of claim 38, wherein the processor is further configured to:quantify an amount of at least one of the one or more contaminants inthe sample medium.
 40. The system of claim 39, wherein the processor isfurther configured to: output an identification and quantification ofthe one or more contaminants in the sample medium to a user interface.41. The system of claim 40, wherein processor is further configured to:output the sample medium quality score to a user interface.
 42. Thesystem of claim 41, wherein the processor and the user interface areintegrated into the system.
 43. The system of claim 41, wherein theprocessor and/or the user interface are remote from the system and/oreach other.
 44. The system of claim 33, wherein the spectral data of thesample medium comprising one or more analytes is deep ultraviolet (UV)spectral data and each of the first plurality of first reference spectrais deep ultraviolet (UV) reference spectra.
 45. The system of claim 31,wherein the processor is a computer, smart phone, or microcontroller.46. The system of claim 31, wherein the system is a portable, handheld,point-and-shoot system.
 47. A system for detecting a target in a watersource comprising: a light-emitting diode operating at a singlewavelength in a deep ultraviolet (UV) range for excitation of a targetin a water source; and a semiconductor photodetector that detectsemission from the excited target and provides a readout if a detectionlevel exceeds a threshold, wherein the system is provided in a housingsized and configured to mate with a top of a drinking glass.
 48. Thesystem of claim 47, wherein the housing has a unitary configuration witha conical shape.
 49. The system of claim 47, wherein the housingcomprises a plurality of components including a base or tripod.
 50. Thesystem of claim 47, wherein the system is a portable, handheld,point-and-shoot system.
 51. The system of claim 47, wherein thethreshold detection level is a total microbial load or a bioburden. 52.The system of claim 47, wherein the emission is in a detection range of300-400 nm.
 53. The system of claim 47, wherein the semiconductorphotodetector is an avalanche photodiode detector or a silicon sensor.54. The system of claim 47, further comprising a processor configured toprocess data received from the semiconductor photodetector.
 55. Thesystem of claim 54, wherein the processor is integrated into the system.56. The system of claim 54, wherein the processor is remote from thesystem.
 57. The system of claim 54, wherein the processor is a computer,smart phone, or microcontroller.
 58. A system for detecting a target ina water source comprising: a light-emitting diode operating at a singlewavelength in a deep ultraviolet (UV) range for excitation of a targetin a water source; and a semiconductor photodetector that detectsemission from the excited target and provides a readout if a detectionlevel exceeds a threshold, wherein the system is configured to becoupled in-line to the water source.
 59. The system of claim 58, whereinthe threshold detection level is a total microbial load or a bioburden.60. The system of claim 58, wherein the emission is in a detection rangeof 300-400 nm.
 61. The system of claim 58, wherein the semiconductorphotodetector is an avalanche photodiode detector or a silicon sensor.62. The system of claim 58, further comprising a processor configured toprocess data received from the semiconductor photodetector.
 63. Thesystem of claim 62, wherein the processor is integrated into the system.64. The system of claim 62, wherein the processor is remote from thesystem.
 65. The system of claim 62, wherein the processor is a computer,smart phone, or microcontroller.
 66. A method of providing informationregarding a medium, the method comprising: providing a system comprisinga light-emitting diode operating at a single wavelength in a deepultraviolet (UV) range for excitation of a target in a medium, and asemiconductor photodetector that detects emission from the excitedtarget, the system configured to be coupled in-line to the medium;exposing a medium comprising one or more target analytes to at least asingle wavelength in the deep UV spectrum from the light-emitting diodeof the system to thereby excite the target analyte in the medium;detecting emission from the excited one or more target analytes via thesemiconductor photodetector to thereby produce emission data; andoutputting a read if the emission data exceeds a threshold detectionlevel, thereby providing information regarding the medium.
 67. Themethod of claim 66, wherein the threshold detection level is a totalmicrobial load or a bioburden.
 68. The method of claim 66, wherein themedium is selected from the group consisting of a biofluid, water, analuminum surface, a stainless steel surface, a granite surface, aceramic surface, a plastic surface, and a metallic surface.
 69. Themethod of claim 66, wherein the target analyte is selected from thegroup consisting of a microorganism, a biomolecule, and a chemical. 70.The method of claim 66, wherein the medium is water and the targetanalyte is one or more pathogens.
 71. The method of claim 66, whereinthe method is performed in Earth's atmospheric conditions.
 72. Themethod of claim 66, wherein the method is performed outside of Earth'satmospheric conditions.
 73. The method of claim 66, wherein processingthe emission data comprises identifying presence of one or more targetanalytes in the medium.
 74. The method of claim 73, wherein processingthe emission data further comprises identifying the one or more targetanalytes in the medium.
 75. The method of claim 74, wherein processingthe emission data further comprises quantifying the one or more targetanalytes in the medium.
 76. The method of claim 66, further comprisingdisplaying on a graphical user interface results of the processing step.77. A method of providing information regarding a medium, the methodcomprising: providing a system comprising a light-emitting diodeoperating at a single wavelength in a deep ultraviolet (UV) range forexcitation of a target in a medium, and a semiconductor photodetectorthat detects emission from the excited target, the system provided in ahousing sized and configured to mate with a top of a drinking glass;exposing a medium comprising one or more target analytes to at least asingle wavelength in the deep UV spectrum from the light-emitting diodeof the system to thereby excite the target analyte in the medium;detecting emission from the excited one or more target analytes via thesemiconductor photodetector to thereby produce emission data; andoutputting a read if the emission data exceeds a threshold detectionlevel, thereby providing information regarding the medium.
 78. Themethod of claim 77, wherein the housing has a unitary configuration witha conical shape.
 79. The method of claim 77, wherein the housingcomprises a plurality of components including a base or tripod.
 80. Themethod of claim 77, wherein the threshold detection level is a totalmicrobial load or a bioburden.
 81. The method of claim 77, wherein themedium is selected from the group consisting of a biofluid, water, analuminum surface, a stainless steel surface, a granite surface, aceramic surface, a plastic surface, and a metallic surface.
 82. Themethod of claim 77, wherein the target analyte is selected from thegroup consisting of a microorganism, a biomolecule, and a chemical. 83.The method of claim 77, wherein the medium is water and the targetanalyte is one or more pathogens.
 84. The method of claim 77, whereinthe method is performed in Earth's atmospheric conditions.
 85. Themethod of claim 77, wherein the method is performed outside of Earth'satmospheric conditions.
 86. The method of claim 77, wherein processingthe emission data comprises identifying presence of one or more targetanalytes in the medium.
 87. The method of claim 86, wherein processingthe emission data further comprises identifying the one or more targetanalytes in the medium.
 88. The method of claim 87, wherein processingthe emission data further comprises quantifying the one or more targetanalytes in the medium.
 89. The method of claim 77, further comprisingdisplaying on a graphical user interface results of the processing step.